DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/01/2026 is being considered by the examiner.
Response to Amendment
Applicant’s remarks, filed 05/21/2026, regarding the objections made to the claims and the 101-rejection submitted in the non-final office action dated 02/24/2026 are withdrawn due to the amendments made to the claims.
Response to Arguments
Applicant’s arguments see remarks, filed 05/21/2026, with respect to claims 1-20 have been considered but are moot because the arguments do not apply to the current combinations of references being used in the current rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 6-8, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over MATSUMOTO et al. (US 20070230763 A1), hereinafter referenced as MATSUMOTO, in view of KUBOTA (US 20080137970 A1), hereinafter referenced as KUBOTA.
Regarding claim 1, MATSUMOTO explicitly teaches a system comprising:
one or more processors (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device.); and
memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device. At this time, the computer-aided image diagnostic processing device 1 may be embodied by previously installing the image diagnostic processing program in the computer device or by recording the image diagnostic processing program in a magnetic disc, a magnetic optical disc, an optical disc, a semiconductor memory or the like or distributing the image diagnostic processing program over a network and installing the image diagnostic processing program in the computer device.), cause the system to:
receive image data including a candidate target nodule (Fig. 3A, illustrates an image including a candidate target nodule illustrated by the circle. Paragraph [0062]-MATSUMOTO discloses the nodule candidate region specifying unit 11 receives the three-dimensional image data acquired by the multi-slice CT 2. Further in paragraph [0064]-MATSUMOTO discloses FIG. 3(a) is a view showing the image to be processed, which is acquired by the multi-slice CT 2. FIG. 3(b) is a view showing the image of the foreground portion segmented from the image to be processed, shown in FIG. 3(a). The nodule exists in a circle in FIG. 3(a).);
segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule (Fig. 2 and 7A-7B. Paragraph [0064]-MATSUMOTO discloses in a step Sa3, the nodule candidate region specifying unit 11 segments the lung region obtained in the step Sa2 into a foreground portion corresponding to lung blood vessel and nodules and a background portion corresponding to the other portion. Further in paragraph [0070]-MATSUMOTO discloses the nodule candidate region specifying unit 11 specifies a set of voxels which is located in the ellipsoidal model after the deformation is finished and belongs to the foreground portion of the lung region as the nodule candidate region.);
after segmenting the image data (Fig. 2, step Sa6 called specify nodule candidate region. Paragraph [0064]-MATSUMOTO discloses in a step Sa3, the nodule candidate region specifying unit 11 segments the lung region obtained in the step Sa2 into a foreground portion corresponding to lung blood vessel and nodules and a background portion corresponding to the other portion. Further in paragraph [0070]-MATSUMOTO discloses the nodule candidate region specifying unit 11 specifies a set of voxels which is located in the ellipsoidal model after the deformation is finished and belongs to the foreground portion of the lung region as the nodule candidate region.), receive a seed point (Fig. 2. Paragraph [0076]-MATSUMOTO discloses in a step Sa8, the judging unit 13 specifies a search reference point in the expanded nodule candidate region. The judging unit 13 generates the search reference point in one-to-one correspondence with the connection component of the expanded nodule candidate region critical portion 104 (wherein step Sa8 is performed after step Sa6 as illustrated in Fig. 2 and wherein a search reference point is a seed point.).);
display a target nodule boundary corresponding to the identified target nodule (Fig. 31 and 33, illustrate displaying a target nodule boundary of an identified target nodule. Paragraph [0197]-MATSUMOTO discloses the nodule and the peripheral structure of the nodule are displayed using a volume rendering technology. At this time, as shown in FIG. 31, the generated ellipsoidal model used in calculating the diminution index is graphic-overlay-displayed by a display method called a wireframe to surround a nodule. Further in paragraph [0198]-MATSUMOTO discloses When the internal structure of the nodule need be observed, a two-dimensional ellipse which is an intersection between a generated ellipse and each section may be displayed on the MPR (Axial, Sagittal, Coronal) section of three perpendicular sections, as shown in FIG. 33.).
MATSUMOTO fails to explicitly teach determine whether the segmented candidate target nodule is within a threshold proximity of the seed point; based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identify the segmented candidate target nodule as an identified target nodule; and.
However, KUBOTA explicitly teaches determine whether the segmented candidate target nodule is within a threshold proximity of the seed point (Fig. 1. Paragraph [0041]-KUBOTA discloses the region is grown conservatively using the distance value of the seed point. The distance value at the seed point is denoted as d. Note that d is the minimum distance from the point to the background voxels. Voxels within the distance of d-1 away from the seed point are clearly within the nodule provided that the seed point is located within the nodule (not necessarily at the center of the nodule). Thus, these voxels are included in the region (wherein the region is a region of a nodule).);
based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identify the segmented candidate target nodule as an identified target nodule (Fig. 1, #108 called output segmentation and size/diameter estimates. Paragraph [0043]-KUBOTA discloses after the region growing, the size/diameter estimates module 107 estimates the volume and diameter of the nodule using the segmentation. Further in paragraph [0009]-KUBOTA discloses outputting the segmentation and an estimate of the measure of the nodule.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of KUBOTA of determine whether the segmented candidate target nodule is within a threshold proximity of the seed point; based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identify the segmented candidate target nodule as an identified target nodule; and.
Wherein having MATSUMOTO’s nodule detection system having determine whether the segmented candidate target nodule is within a threshold proximity of the seed point; based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identify the segmented candidate target nodule as an identified target nodule; and.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KUBOTA relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KUBOTA implementations include improving the performance of a Computer-Aided Diagnosis (CAD) system and aiding radiologists in assessing the growth of a nodule. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KUBOTA (US 20080137970 A1), Paragraph [0017].
Regarding claim 6, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO further explicitly teaches wherein receiving the seed point includes receiving three-dimensional coordinates corresponding to the seed point (Fig. 2. Paragraph [0076]-MATSUMOTO discloses the judging unit 13 determines the position of the voxel which applies the start point of the selected centrifugal direction segment 106 as the search reference point 107 corresponding to the connection component (wherein the image that the voxel is a part of is a 3D image and a position of a voxel is a point in the 3D image.).).
Regarding claim 7, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO further explicitly teaches wherein the computer readable instructions, when executed by the one or more processors (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device. At this time, the computer-aided image diagnostic processing device 1 may be embodied by previously installing the image diagnostic processing program in the computer device or by recording the image diagnostic processing program in a magnetic disc, a magnetic optical disc, an optical disc, a semiconductor memory or the like or distributing the image diagnostic processing program over a network and installing the image diagnostic processing program in the computer device.),
MATSUMOTO fails to explicitly teach further cause the system to provide guidance for positioning the seed point.
However, KUBOTA explicitly teaches further cause the system to provide guidance for positioning the seed point (Fig. 1. Paragraph [0018]-KUBOTA discloses the nodules were marked by expert radiologists and represented by positions (marked positions) in the CT data. Further in paragraph [0026]-KUBOTA discloses seed point selection module 103 relocates the marked position to the center of the nodule. The relocated position is a seed point (wherein relocating the marked position is providing guidance for positioning the seed point).).
MK motivation: Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of KUBOTA of further cause the system to provide guidance for positioning the seed point.
Wherein having MATSUMOTO’s nodule detection system further cause the system to provide guidance for positioning the seed point.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KUBOTA relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KUBOTA implementations include improving the performance of a Computer-Aided Diagnosis (CAD) system and aiding radiologists in assessing the growth of a nodule. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KUBOTA (US 20080137970 A1), Paragraph [0017].
Regarding claim 8, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 7,
MATSUMOTO further explicitly teaches wherein the guidance includes a graphical representation of a region that includes the segmented candidate target nodule (Fig. 31, illustrates a graphical representation of a region that includes the segmented candidate target nodule. Paragraph [0197]-MATSUMOTO discloses at this time, as shown in FIG. 31, the generated ellipsoidal model used in calculating the diminution index is graphic-overlay-displayed by a display method called a wireframe to surround a nodule.).
Regarding claim 17, MATSUMOTO explicitly teaches a non-transitory machine-readable medium comprising a plurality of machine-readable instructions which when executed by one or more processors associated with a planning workstation are adapted to cause the one or more processors to perform a method comprising (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device. At this time, the computer-aided image diagnostic processing device 1 may be embodied by previously installing the image diagnostic processing program in the computer device or by recording the image diagnostic processing program in a magnetic disc, a magnetic optical disc, an optical disc, a semiconductor memory or the like or distributing the image diagnostic processing program over a network and installing the image diagnostic processing program in the computer device.):
receiving image data including a candidate target nodule (Fig. 3A, illustrates an image including a candidate target nodule illustrated by the circle. Paragraph [0062]-MATSUMOTO discloses the nodule candidate region specifying unit 11 receives the three-dimensional image data acquired by the multi-slice CT 2. Further in paragraph [0064]-MATSUMOTO discloses FIG. 3(a) is a view showing the image to be processed, which is acquired by the multi-slice CT 2. FIG. 3(b) is a view showing the image of the foreground portion segmented from the image to be processed, shown in FIG. 3(a). The nodule exists in a circle in FIG. 3(a).);
segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule (Fig. 2 and 7A-7B. Paragraph [0064]-MATSUMOTO discloses in a step Sa3, the nodule candidate region specifying unit 11 segments the lung region obtained in the step Sa2 into a foreground portion corresponding to lung blood vessel and nodules and a background portion corresponding to the other portion. Further in paragraph [0070]-MATSUMOTO discloses the nodule candidate region specifying unit 11 specifies a set of voxels which is located in the ellipsoidal model after the deformation is finished and belongs to the foreground portion of the lung region as the nodule candidate region.);
after segmenting the image data (Fig. 2, step Sa6 called specify nodule candidate region. Paragraph [0064]-MATSUMOTO discloses in a step Sa3, the nodule candidate region specifying unit 11 segments the lung region obtained in the step Sa2 into a foreground portion corresponding to lung blood vessel and nodules and a background portion corresponding to the other portion. Further in paragraph [0070]-MATSUMOTO discloses the nodule candidate region specifying unit 11 specifies a set of voxels which is located in the ellipsoidal model after the deformation is finished and belongs to the foreground portion of the lung region as the nodule candidate region.), receiving a seed point (Fig. 2. Paragraph [0076]-MATSUMOTO discloses in a step Sa8, the judging unit 13 specifies a search reference point in the expanded nodule candidate region. The judging unit 13 generates the search reference point in one-to-one correspondence with the connection component of the expanded nodule candidate region critical portion 104 (wherein step Sa8 is performed after step Sa6 as illustrated in Fig. 2 and wherein a search reference point is a seed point.).);
displaying a target nodule boundary corresponding to the identified target nodule display a target nodule boundary corresponding to the identified target nodule (Fig. 31 and 33, illustrate displaying a target nodule boundary of an identified target nodule. Paragraph [0197]-MATSUMOTO discloses the nodule and the peripheral structure of the nodule are displayed using a volume rendering technology. At this time, as shown in FIG. 31, the generated ellipsoidal model used in calculating the diminution index is graphic-overlay-displayed by a display method called a wireframe to surround a nodule. Further in paragraph [0198]-MATSUMOTO discloses When the internal structure of the nodule need be observed, a two-dimensional ellipse which is an intersection between a generated ellipse and each section may be displayed on the MPR (Axial, Sagittal, Coronal) section of three perpendicular sections, as shown in FIG. 33.).
MATSUMOTO fails to explicitly teach determining whether the segmented candidate target nodule is within a threshold proximity of the seed point; based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identifying the segmented candidate target nodule as an identified target nodule; and.
However, KUBOTA explicitly teaches determining whether the segmented candidate target nodule is within a threshold proximity of the seed point (Fig. 1. Paragraph [0041]-KUBOTA discloses the region is grown conservatively using the distance value of the seed point. The distance value at the seed point is denoted as d. Note that d is the minimum distance from the point to the background voxels. Voxels within the distance of d-1 away from the seed point are clearly within the nodule provided that the seed point is located within the nodule (not necessarily at the center of the nodule). Thus, these voxels are included in the region (wherein the region is a region of a nodule).);
based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identifying the segmented candidate target nodule as an identified target nodule (Fig. 1, #108 called output segmentation and size/diameter estimates. Paragraph [0043]-KUBOTA discloses after the region growing, the size/diameter estimates module 107 estimates the volume and diameter of the nodule using the segmentation. Further in paragraph [0009]-KUBOTA discloses outputting the segmentation and an estimate of the measure of the nodule.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO of a non-transitory machine-readable medium comprising a plurality of machine-readable instructions which when executed by one or more processors associated with a planning workstation are adapted to cause the one or more processors to perform a method comprising: receiving image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receiving a seed point; with the teachings of KUBOTA of determining whether the segmented candidate target nodule is within a threshold proximity of the seed point; based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identifying the segmented candidate target nodule as an identified target nodule; and.
Wherein having MATSUMOTO’s nodule detection system having determining whether the segmented candidate target nodule is within a threshold proximity of the seed point; based on a determination that the segmented candidate target nodule is within the threshold proximity of the seed point, identifying the segmented candidate target nodule as an identified target nodule; and.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KUBOTA relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KUBOTA implementations include improving the performance of a Computer-Aided Diagnosis (CAD) system and aiding radiologists in assessing the growth of a nodule. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KUBOTA (US 20080137970 A1), Paragraph [0017].
Claims 3-5 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over MATSUMOTO et al. (US 20070230763 A1), hereinafter referenced as MATSUMOTO, in view of KUBOTA (US 20080137970 A1), hereinafter referenced as KUBOTA, and further in view of KLUCKNER et al. (US 20180174311 A1), hereinafter referenced as KLUCKNER.
Regarding claim 3, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO in view of KUBOTA fail to explicitly teach wherein the image data includes pre-operative image data.
However, KLUCKNER explicitly teaches wherein the image data includes pre-operative image data (Fig. 1. Paragraph [0014]-KLUCKNER discloses at step 102, pre-operative 3D medical image data of a patient is received. The pre-operative 3D medical image data is acquired prior to the surgical procedure. The 3D medical image data can include a 3D medical image volume, which can be acquired using any imaging modality, such as computed tomography (CT), magnetic resonance (MR), or positron emission tomography (PET).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of KLUCKNER of wherein the image data includes pre-operative image data.
Wherein having MATSUMOTO’s nodule detection system having wherein the image data includes pre-operative image data.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KLUCKNER relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KLUCKNER enables the acquisition of scene specific semantic information for acquired video frames based on segmented pre-operative medical image data. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KLUCKNER et al. (US 20180174311 A1), Paragraph [0012].
Regarding claim 4, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO in view of KUBOTA fail to explicitly teach wherein the image data includes intra-operative image data.
However, KLUCKNER explicitly teaches wherein the image data includes intra-operative image data (Fig. 1. Paragraph [0016]-KLUCKNER at step 104, an intra-operative image stream is received. The intra-operative image stream can also be referred to as a video, with each frame of the video being an intra-operative image.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of KLUCKNER of wherein the image data includes intra-operative image data.
Wherein having MATSUMOTO’s nodule detection system having wherein the image data includes intra-operative image data.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KLUCKNER relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KLUCKNER enables the acquisition of scene specific semantic information for acquired video frames based on segmented pre-operative medical image data. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KLUCKNER et al. (US 20180174311 A1), Paragraph [0012].
Regarding claim 5, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO in view of KUBOTA fail to explicitly teach wherein the image data includes pre-operative and intra-operative image data.
However, KLUCKNER explicitly teaches wherein the image data includes pre-operative (Fig. 1. Paragraph [0014]-KLUCKNER discloses at step 102, pre-operative 3D medical image data of a patient is received. The pre-operative 3D medical image data is acquired prior to the surgical procedure. The 3D medical image data can include a 3D medical image volume, which can be acquired using any imaging modality, such as computed tomography (CT), magnetic resonance (MR), or positron emission tomography (PET).) and intra-operative image data (Fig. 1. Paragraph [0016]-KLUCKNER at step 104, an intra-operative image stream is received. The intra-operative image stream can also be referred to as a video, with each frame of the video being an intra-operative image.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of KLUCKNER of wherein the image data includes pre-operative and intra-operative image data.
Wherein having MATSUMOTO’s nodule detection system having wherein the image data includes pre-operative and intra-operative image data.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KLUCKNER relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KLUCKNER enables the acquisition of scene specific semantic information for acquired video frames based on segmented pre-operative medical image data. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KLUCKNER et al. (US 20180174311 A1), Paragraph [0012].
Regarding claim 19, MATSUMOTO in view of KUBOTA explicitly teach the non-transitory machine-readable medium of claim 17,
MATSUMOTO in view of KUBOTA fail to explicitly teach wherein the image data includes pre-operative image data.
However, KLUCKNER explicitly teaches wherein the image data includes pre-operative image data (Fig. 1. Paragraph [0014]-KLUCKNER discloses at step 102, pre-operative 3D medical image data of a patient is received. The pre-operative 3D medical image data is acquired prior to the surgical procedure. The 3D medical image data can include a 3D medical image volume, which can be acquired using any imaging modality, such as computed tomography (CT), magnetic resonance (MR), or positron emission tomography (PET).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a non-transitory machine-readable medium comprising a plurality of machine-readable instructions which when executed by one or more processors associated with a planning workstation are adapted to cause the one or more processors to perform a method comprising: receiving image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receiving a seed point; with the teachings of KLUCKNER of wherein the image data includes pre-operative image data.
Wherein having MATSUMOTO’s nodule detection system having wherein the image data includes pre-operative image data.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KLUCKNER relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KLUCKNER enables the acquisition of scene specific semantic information for acquired video frames based on segmented pre-operative medical image data. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KLUCKNER et al. (US 20180174311 A1), Paragraph [0012].
Regarding claim 20, MATSUMOTO in view of KUBOTA explicitly teach the non-transitory machine-readable medium of claim 17,
MATSUMOTO in view of KUBOTA fail to explicitly teach wherein the image data includes intra-operative image data.
However, KLUCKNER explicitly teaches wherein the image data includes intra-operative image data (Fig. 1. Paragraph [0016]-KLUCKNER at step 104, an intra-operative image stream is received. The intra-operative image stream can also be referred to as a video, with each frame of the video being an intra-operative image.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a non-transitory machine-readable medium comprising a plurality of machine-readable instructions which when executed by one or more processors associated with a planning workstation are adapted to cause the one or more processors to perform a method comprising: receiving image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receiving a seed point; with the teachings of KLUCKNER of wherein the image data includes intra-operative image data.
Wherein having MATSUMOTO’s nodule detection system having wherein the image data includes intra-operative image data.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and KLUCKNER relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while KLUCKNER enables the acquisition of scene specific semantic information for acquired video frames based on segmented pre-operative medical image data. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and KLUCKNER et al. (US 20180174311 A1), Paragraph [0012].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over MATSUMOTO et al. (US 20070230763 A1), hereinafter referenced as MATSUMOTO, in view of KUBOTA (US 20080137970 A1), hereinafter referenced as KUBOTA, and further in view of COLLINS et al. (US 20060274928 A1), hereinafter referenced as COLLINS.
Regarding claim 9, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO further explicitly teaches wherein the computer readable instructions, when executed by the one or more processors (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device. At this time, the computer-aided image diagnostic processing device 1 may be embodied by previously installing the image diagnostic processing program in the computer device or by recording the image diagnostic processing program in a magnetic disc, a magnetic optical disc, an optical disc, a semiconductor memory or the like or distributing the image diagnostic processing program over a network and installing the image diagnostic processing program in the computer device.),
MATSUMOTO in view of KUBOTA fail to explicitly teach further cause the system to receive user input to edit the displayed target nodule boundary, generate a revised target nodule boundary, and display the revised target nodule boundary.
However, COLLINS explicitly teaches further cause the system to receive user input to edit the displayed target nodule boundary (Fig. 4A-4B. Paragraph [0054]-COLLINS discloses the user can also refine a selected candidate by editing the segmentation outline 404. To do this, a user may edit existing control points or defining additional control points on a segmentation outline. The user may modify a displayed segmentation candidate by editing one or several control points 410 of the segmentation outline 404 to manually segment an ROI (see FIG. 4B). The user may also modify a displayed candidate by defining new control point(s).), generate a revised target nodule boundary (Fig. 4A-4B. Paragraph [0054]-COLLINS discloses after the user finishes editing existing control point(s) or adding new control point(s), the system displays a modified segmentation outline for the user to confirm (wherein the modified segmentation outline is a revised target nodule boundary).), and display the revised target nodule boundary (Fig. 4A-4B. Paragraph [0054]-COLLINS discloses after the user finishes editing existing control point(s) or adding new control point(s), the system displays a modified segmentation outline for the user to confirm (wherein the modified segmentation outline is a revised target nodule boundary).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of COLLINS of further cause the system to receive user input to edit the displayed target nodule boundary, generate a revised target nodule boundary, and display the revised target nodule boundary.
Wherein having MATSUMOTO’s nodule detection system having further cause the system to receive user input to edit the displayed target nodule boundary, generate a revised target nodule boundary, and display the revised target nodule boundary.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and COLLINS relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while COLLINS the system automatically re-computes its automated assessment, to give the user an immediate feedback so the user can make a more accurate and improved diagnosis. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and COLLINS et al. (US 20060274928 A1), Paragraph [0066].
Claims 10-11 is rejected under 35 U.S.C. 103 as being unpatentable over MATSUMOTO et al. (US 20070230763 A1), hereinafter referenced as MATSUMOTO, in view of KUBOTA (US 20080137970 A1), hereinafter referenced as KUBOTA, and further in view of COLLINS et al. (US 20060274928 A1), hereinafter referenced as COLLINS, and further in view of MAO et al. (US 20030152262 A1), hereinafter referenced as MAO.
Regarding claim 10, MATSUMOTO in view of KUBOTA and further in view of COLLINS explicitly teach the system of claim 9,
MATSUMOTO in view of KUBOTA and further in view of COLLINS fail to explicitly teach wherein editing the displayed target nodule boundary includes extending a boundary wall based on a cursor being inside the target nodule boundary.
However, MAO explicitly teaches wherein editing the displayed target nodule boundary includes extending a boundary wall based on a cursor being inside the target nodule boundary (Fig. 1. Paragraph [0054]-MAO discloses the user physically retracts the mouse, causing a decrease in the iteration variable and a corresponding retraction in the region. By physically moving the mouse, the user may adjust the iteration variable, thereby extending or retracting the region, until an optimal region is reached. The optimal region selects as much of the feature of interest as is possible without selecting a significant number of points corresponding to unwanted artifacts.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA and further in view of COLLINS of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of MAO of wherein editing the displayed target nodule boundary includes extending a boundary wall based on a cursor being inside the target nodule boundary.
Wherein having MATSUMOTO’s nodule detection system having wherein editing the displayed target nodule boundary includes extending a boundary wall based on a cursor being inside the target nodule boundary.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and MAO relate to detecting regions of interest in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while MAO there remains a need for a system and method of quickly and easily analyzing and manipulating computer-generated images in order to filter out as much irrelevant information as possible from the images and display only the region of interest. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and MAO et al. (US 20030152262 A1), Paragraph [0005].
Regarding claim 11, MATSUMOTO in view of KUBOTA and further in view of COLLINS explicitly teach the system of claim 9,
MATSUMOTO in view of KUBOTA and further in view of COLLINS fail to explicitly teach wherein editing the displayed target nodule boundary includes retracting a boundary wall based on a cursor being outside the target nodule boundary.
However, MAO explicitly teaches wherein editing the displayed target nodule boundary includes retracting a boundary wall based on a cursor being outside the target nodule boundary (Fig. 1. Paragraph [0054]-MAO discloses the user physically retracts the mouse, causing a decrease in the iteration variable and a corresponding retraction in the region. By physically moving the mouse, the user may adjust the iteration variable, thereby extending or retracting the region, until an optimal region is reached. The optimal region selects as much of the feature of interest as is possible without selecting a significant number of points corresponding to unwanted artifacts.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA and further in view of COLLINS of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of MAO of wherein editing the displayed target nodule boundary includes retracting a boundary wall based on a cursor being outside the target nodule boundary.
Wherein having MATSUMOTO’s nodule detection system having wherein editing the displayed target nodule boundary includes retracting a boundary wall based on a cursor being outside the target nodule boundary.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and MAO relate to detecting regions of interest in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while MAO there remains a need for a system and method of quickly and easily analyzing and manipulating computer-generated images in order to filter out as much irrelevant information as possible from the images and display only the region of interest. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and MAO et al. (US 20030152262 A1), Paragraph [0005].
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over MATSUMOTO et al. (US 20070230763 A1), hereinafter referenced as MATSUMOTO, in view of KUBOTA (US 20080137970 A1), hereinafter referenced as KUBOTA, and further in view of YAMAGATA et al. (US 20070286469 A1), hereinafter referenced as YAMAGATA.
Regarding claim 12, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO further explicitly teaches wherein the computer readable instructions, when executed by the one or more processors (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device. At this time, the computer-aided image diagnostic processing device 1 may be embodied by previously installing the image diagnostic processing program in the computer device or by recording the image diagnostic processing program in a magnetic disc, a magnetic optical disc, an optical disc, a semiconductor memory or the like or distributing the image diagnostic processing program over a network and installing the image diagnostic processing program in the computer device.),
MATSUMOTO in view of KUBOTA fail to explicitly teach further cause the system to display a default target indicator surrounding the seed point, wherein the displaying is based on a determination that the segmented candidate target nodule is not within the threshold proximity of the seed point.
However, YAMAGATA explicitly teaches further cause the system to display a default target indicator surrounding the seed point (Fig. 24, illustrates default indicators surrounding seed points, called mark #272. Paragraph [0146]-YAMAGATA discloses the mark 272 represents the position of the nodule candidate determined by the diagnosing reading doctor. The mark 272 has a solid-line cross shape and a yellow display color (wherein a mark is a seed point).),
wherein the displaying is based on a determination that the segmented candidate target nodule is not within the threshold proximity of the seed point (Fig. 25. Paragraph [0147]-YAMAGATA discloses of positions of nodule candidate regions determined as nodules by the determination unit 13, a position whose minimum distance with respect to the position specified by the diagnosing reading doctor is equal to or above a threshold value is represented by the mark 281, and a position whose minimum distance is less than the threshold value is represented by the mark 282.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of YAMAGATA of further cause the system to display a default target indicator surrounding the seed point, wherein the displaying is based on a determination that the segmented candidate target nodule is not within the threshold proximity of the seed point.
Wherein having MATSUMOTO’s nodule detection system having further cause the system to display a default target indicator surrounding the seed point, wherein the displaying is based on a determination that the segmented candidate target nodule is not within the threshold proximity of the seed point.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and YAMAGATA relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while YAMAGATA there has been a demand for enabling a medical doctor to judge whether an abnormality candidate region included in an image showing the inside of a subject is an anatomic abnormality like a nodule is demanded. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and YAMAGATA et al. (US 20070286469 A1), Paragraph [0012].
Claim 13-14 is rejected under 35 U.S.C. 103 as being unpatentable over MATSUMOTO et al. (US 20070230763 A1), hereinafter referenced as MATSUMOTO, in view of KUBOTA (US 20080137970 A1), hereinafter referenced as KUBOTA, and further in view of YAMAGATA et al. (US 20070286469 A1), hereinafter referenced as YAMAGATA, and further in view of VISWANATHAN et al. (US 20060041180 A1), hereinafter referenced as VISWANATHAN.
Regarding claim 13, MATSUMOTO in view of KUBOTA and further in view of YAMAGATA explicitly teach the system of claim 12,
MATSUMOTO in view of KUBOTA and further in view of YAMAGATA fail to explicitly teach wherein the default target indicator has a three-dimensional ellipsoid shape.
However, VISWANATHAN explicitly teaches wherein the default target indicator has a three- dimensional ellipsoid shape (Fig. 33 Paragraph [0199]-VISWANATHAN discloses the object navigation pane 836 has a representation 940 of a three-dimensional object. This three dimensional object is preferably a sphere, but it could be some other shape such as an ellipse or a cube. There are preferably indicators on the surface of the three dimensional object to indicate the corresponding directions in the operating region in the subject).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA and further in view of YAMAGATA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of VISWANATHAN of wherein the default target indicator has a three- dimensional ellipsoid shape.
Wherein having MATSUMOTO’s nodule detection system having wherein the default target indicator has a three- dimensional ellipsoid shape.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and VISWANATHAN relate to analyzing medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while VISWANATHAN provides an easy way for a physician to visualize the procedure site. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and VISWANATHAN et al. (US 20060041180 A1), Paragraph [0003].
Regarding claim 14, MATSUMOTO in view of KUBOTA and further in view of YAMAGATA and further in view of VISWANATHAN explicitly teach the system of claim 13,
MATSUMOTO in view of KUBOTA and further in view of YAMAGATA fail to explicitly teach wherein the three-dimensional ellipsoid shape is adjustable.
However, VISWANATHAN explicitly teaches wherein the three-dimensional ellipsoid shape is adjustable (Fig. 33 Paragraph [0199]-VISWANATHAN discloses the object navigation pane 836 has a representation 940 of a three-dimensional object. This three dimensional object is preferably a sphere, but it could be some other shape such as an ellipse or a cube. There are preferably indicators on the surface of the three dimensional object to indicate the corresponding directions in the operating region in the subject Further in paragraph [0202]-VISWANATHAN discloses the rotation button 948 toggles between a rotation mode in which the cursor can be manipulated by a control device such as mouse, joystick, or keyboard to grab and rotate the representation 940 of the object, and a selection mode in which the cursor can be manipulated by a control device such as a mouse, joystick, or keyboard, to select a point of the surface of the representation 940 of the object.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA and further in view of YAMAGATA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of VISWANATHAN of wherein the three-dimensional ellipsoid shape is adjustable.
Wherein having MATSUMOTO’s nodule detection system having wherein the three-dimensional ellipsoid shape is adjustable.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and VISWANATHAN relate to analyzing medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while VISWANATHAN provides an easy way for a physician to visualize the procedure site. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and VISWANATHAN et al. (US 20060041180 A1), Paragraph [0003].
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over MATSUMOTO et al. (US 20070230763 A1), hereinafter referenced as MATSUMOTO, in view of KUBOTA (US 20080137970 A1), hereinafter referenced as KUBOTA, and further in view of DOUGHERTY et al. (US 20190318483 A1), hereinafter referenced as DOUGHERTY.
Regarding claim 15, MATSUMOTO in view of KUBOTA explicitly teach the system of claim 1,
MATSUMOTO further explicitly teaches wherein the computer readable instructions, when executed by the one or more processors (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device. At this time, the computer-aided image diagnostic processing device 1 may be embodied by previously installing the image diagnostic processing program in the computer device or by recording the image diagnostic processing program in a magnetic disc, a magnetic optical disc, an optical disc, a semiconductor memory or the like or distributing the image diagnostic processing program over a network and installing the image diagnostic processing program in the computer device.),
MATSUMOTO in view of KUBOTA fail to explicitly teach further cause the system to receive a target engagement location indicator for the identified target nodule.
However, DOUGHERTY explicitly teaches further cause the system to receive a target engagement location indicator for the identified target nodule (Fig. 7, #420 called target tissue. Paragraph [0081]-DOUGHERTY discloses image analysis system 50 and/or navigation system 70 may calculate navigation pathway 416 from the entry of the airway to the location of target tissue 420 (wherein target tissue #420 is the target engagement location indicator).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of DOUGHERTY of further cause the system to receive a target engagement location indicator for the identified target nodule.
Wherein having MATSUMOTO’s nodule detection system having further cause the system to receive a target engagement location indicator for the identified target nodule.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and DOUGHERTY relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while DOUGHERTY a need remains for improved medical devices and procedures, including improved segmentation and image processing, for visualizing, accessing, locating, real-time confirming, sampling, and manipulating a target tissue or area. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and DOUGHERTY et al. (US 20190318483 A1), Paragraph [0004].
Regarding claim 16, MATSUMOTO in view of KUBOTA and further in view of DOUGHERTY explicitly teach the system of claim 15,
MATSUMOTO further explicitly teaches wherein the computer readable instructions, when executed by the one or more processors (Fig. 1. Paragraph [0057]-MATSUMOTO discloses the nodule candidate region specifying unit 11, the expanded nodule candidate region specifying unit 12 and the judging unit 13 can be embodied by executing an image diagnostic processing program on a processor mounted in the computer device. At this time, the computer-aided image diagnostic processing device 1 may be embodied by previously installing the image diagnostic processing program in the computer device or by recording the image diagnostic processing program in a magnetic disc, a magnetic optical disc, an optical disc, a semiconductor memory or the like or distributing the image diagnostic processing program over a network and installing the image diagnostic processing program in the computer device.),
MATSUMOTO in view of KUBOTA fail to explicitly teach further cause the system to plan an instrument route to the target engagement location indicator.
However, DOUGHERTY explicitly teaches further cause the system to plan an instrument route to the target engagement location indicator (Fig. 7, #416 called navigation pathway(s). Paragraph [0081]-DOUGHERTY discloses image analysis system 50 and/or navigation system 70 may calculate navigation pathway 416 from the entry of the airway to the location of target tissue 420. In certain embodiments, navigation pathway 416 may be an optimal endobronchial path to a target tissue. For example, navigation pathway 416 may represent the closest distance and/or closest angle to the target tissue. A physician or other healthcare professional may follow navigation pathway 416 during an image guided intervention to reach the location of target tissue 420 (wherein target tissue #420 is the target engagement location indicator).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of MATSUMOTO in view of KUBOTA of a system comprising: one or more processors; and memory having computer readable instructions stored thereon, the computer readable instructions, when executed by the one or more processors, cause the system to: receive image data including a candidate target nodule; segmenting the image data by partitioning voxels of the image data to generate a segmented candidate target nodule; after segmenting the image data, receive a seed point; with the teachings of DOUGHERTY of further cause the system to plan an instrument route to the target engagement location indicator.
Wherein having MATSUMOTO’s nodule detection system having further cause the system to plan an instrument route to the target engagement location indicator.
The motivation behind the modification would have been to obtain a lung nodule detection system that enhances the ability to accurately detect and properly identify potential/real nodules in medical images. Since both MATSUMOTO and DOUGHERTY relate to detecting nodules in medical images, wherein MATSUMOTO it is possible to reduce the burden of a doctor and to prevent the nodule from being overlooked, while DOUGHERTY a need remains for improved medical devices and procedures, including improved segmentation and image processing, for visualizing, accessing, locating, real-time confirming, sampling, and manipulating a target tissue or area. Please see MATSUMOTO et al. (US 20070230763 A1), Paragraph [0099], and DOUGHERTY et al. (US 20190318483 A1), Paragraph [0004].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant's disclosure.
CASE et al. (US 20130317352 A1) - The present disclosure is directed to a planning and navigation method. The planning and method includes obtaining and rendering a plurality of images. The plurality of images are segmented to demarcate a target area. A treatment plan is determined based on the target area. The navigation method includes obtaining an ultrasound image of a scan plane including the target and obtaining a fiducial image of a fiducial pattern disposed on an ultrasound device. The obtained fiducial image is corrected and a correspondence between the fiducial image and a model image is found. A position of the surgical device is transformed to model coordinates. Then the ultrasound image and a virtual image of the surgical device is displayed to allow a surgeon to navigate the surgical device to the target using the displayed ultrasound image and the virtual image. The target is treated based on the treatment plan…Abstract, Fig. 8-9.
BROWN (US 20150254842 A1) - A system and method for automatically segmenting a computed tomography (CT) image of a patient's lung. The method includes the steps of segmenting the CT image to acquire one or more lung regions, intensity thresholding the lung regions to generate a mask region comprising high-intensity regions corresponding to anatomical structures within the lung regions, computing a Euclidean distance map of the mask region, performing watershed segmentation of the Euclidean distance map to generate one or more sub-regions, identifying a seed point for each sub region, growing candidate regions from the seed point of each sub-region, and classifying one or more candidate regions as a lung nodule based on one or more geometric features of the candidate regions…Abstract, Fig. 1.
DEHMESHKI et al. (US 7574031 B2) - A method of detecting the extent of a lung nodule in a scan image comprises fine segmenting (14) a region around the nodule into foreground and background, filling holes within foreground areas (16), region growing the foreground (18) to identify an initial region of interest, determining a mask (20) by enlarging the initial region to contain background and to exclude other foreground regions, determining a spatial map (26) of connectivity within the mask to a point within the initial region, region growing (28) within the mask and determining the maximum edge contrast (30) during region growing. The region of maximum edge contrast identifies the extent of the nodule (32). The position of the nodule may a seed point identified by the user (12). Most preferably, an optimum seed point is determined by iterative determination of seed points (22, 24) so that the determined extent of the nodule is substantially independent of the precise location of the seed point identified by the user…Abstract, Fig. 2.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ETHAN N WOLFSON/ Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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