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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on July 28, 2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 28, 2026 and August 18, 2026 was filed after the mailing date of the Notice of Allowance on July 16, 2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The amendment filed on July 28, 2026 has been entered.
In view of the amendment to the specification, a replacement drawing of FIG. 1 has been acknowledged.
Response to Arguments
Applicant’s arguments, see pages 1-2 of Remarks, filed February 24, 2026 have been fully considered.
Regarding to arguments, Applicants state in page 2 of Remarks that “...
Applicant submits that one of ordinary skill in the art would not have found the claimed invention obvious. For example, amended claim 37 recites in part “wherein the contouring guidelines refer to one or more of: anatomical boundaries between structures, other anatomical structures, other anatomical features, and wherein the feedback is provided by annotation of the one or more contours to indicate a type of error with the contour” (emphasis added).
...”.
Examiner replies:
The examiner has considered the documents listed in IDS. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the combination of the previous cited prior art references by applying the teachings of “ALTMAN et al., "A Framework for Automated Contour Quality Assurance in Radiation Therapy Including Adaptive Techniques," Physics in Medicine & Biology, Volume 60, Pages 5199-5209. June 2015.” Listed in IDS submitted on July 28, 2026 to obtain the claim invention.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 39 and 58 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Dependent claim 39 depends upon independent claim 37. The claim 37 recites “... in response to a determination that the at least one of the one or more contours conforms to the guidelines, providing feedback about a quality of the one or more contours ...”. However, the dependent claim 39 further recites “... providing feedback about a quality of the one or more contours”. The issue is persons of ordinary skill in the art reading the specification is not able to understand how to distinguish two of “a quality” of the one or more contours. Therefore, the examiner deems the claim indefinite as it fails to particularly point out and distinctly claim what Applicant regards as the invention. Accordingly, the claim is rejected under U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Dependent claim 58 depends upon independent claim 56. The claim 56 recites “... in response to a determination that the at least one of the one or more contours conforms to the guidelines, providing feedback about a quality of the one or more contours ...”. However, the dependent claim 58 further recites “... providing feedback about a quality of the one or more contours”. The issue is persons of ordinary skill in the art reading the specification is not able to understand how to distinguish two of “a quality” of the one or more contours. Therefore, the examiner deems the claim indefinite as it fails to particularly point out and distinctly claim what Applicant regards as the invention. Accordingly, the claim is rejected under U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim Rejections - 35 USC § 103
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 of this title, 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 37-43, 47-53 and 56-58 are rejected under 35 U.S.C. 103 as being unpatentable over Piper (U.S. Patent Application Publication 2011/0268330 A1) in view of Altman et al (“ALTMAN et al., "A Framework for Automated Contour Quality Assurance in Radiation Therapy Including Adaptive Techniques," Physics in Medicine & Biology, Volume 60, Pages 5199-5209. June 2015).
Regarding claim 37, Piper discloses a method for reviewing previously contoured images in a contouring system, comprising:
loading a medical image and one of more contours (Paragraph [0030], FIG. 4 is a flow diagram of another example method 400 for contouring a set of medical images. In step 402, the method is initialized with an initial source image with associated source contour data ...; paragraph [0002], medical images, such CT (computed tomography), MR (magnetic resonance), US (ultrasound), or PET (positron emission tomography) scans, are regularly contoured to identify certain pieces of anatomy within the image. For example, a radiologist or oncologist may contour a medical image to identify a tumor within the image; paragraph [0032], in other embodiments, a non-consecutive slice may be contoured by direct deformation from the previously contoured target image or by computing and concatenating deformations pairwise through a chain of consecutive slices ... For example: FIG. 1; paragraph [0012], an example system 100 for contouring a set of medical images ...; paragraph [0013], the plurality of medical images are loaded into the image database 108 for contouring ...; paragraph [0020], the user could start with a contoured source slice in the middle of the set of images 108 ...) associated with a structure represented in the medical image (Paragraph [0002], medical images, such CT (computed tomography), MR (magnetic resonance), US (ultrasound), or PET (positron emission tomography) scans, are regularly contoured to identify certain pieces of anatomy within the image. For example, a radiologist or oncologist may contour a medical image to identify a tumor within the image);
automatically determining if at least one of the one or more contours conforms to guidelines specific to the structure (Paragraph [0030], in step 404, a target image is identified for contouring based on the source image and source contour. In step 406, a deformation algorithm is applied to the source and target images to generate deformation field data that indicates how one or more objects within the images have changed from the source image to the target image. The deformation field data is then applied to the source contour data in step 408 to automatically generate contour data for the target image by transforming the source contour to match the changes from the source image to the target image); and
in response to a determination that the at least one of the one or more contours conforms to the guidelines (Paragraph [0031], at decision step 410, a user input is received to either accept or modify the automatically generated target contour),
wherein the contouring guidelines refer to one or more of: anatomical boundaries between structures, other anatomical structures, other anatomical features (Paragraph [0002], ... a radiologist or oncologist may contour a medical image to identify a tumor within the image. Software tools are available to assist in this type of "manual" contouring, in which the physician uses the software to create the contour by tracing the boundary of the object or objects within the image).
However, Piper doses not specifically disclose providing feedback about a quality of the one or more contours, and
wherein the feedback is provided by annotation of the one or more contours to indicate a type of error with the contour.
In additional, Altman discloses (Abstract, contouring of targets and normal tissues is one of the largest sources of variability in radiation therapy treatment plans. Contours thus require a time intensive and error-prone quality assurance (QA) evaluation, limitations which also impair the facilitation of adaptive radiotherapy (ART). Here, an automated system for contour QA is developed using historical data (the ‘knowledge base’) ...) providing feedback about a quality of the one or more contours (Page 5201; Figure 1. Schematic of the automatic contour QA methodology, which uses a database of verified contour datasets to describe the evaluation metrics and decision criteria (left side). New contours are input and evaluated as described on the right side of the diagram. A feedback loop shows how contours validated as accurate by the system can be added to the database and used for further evaluation of additional contour data. See text for more details), and
wherein the feedback is provided by annotation of the one or more contours to indicate a type of error with the contour (Page 5204; Table 3. Summary of results from the pilot study. Errors are distributed by structures and the numbers of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) are reported, as with sensitivity (TP/(TP + FN)) and specificity (TN/(TN + FP)) Errors are also presented broken down by type ... Metric computation and evaluation criteria analysis were performed on Pinnacle-exported DICOM data in MATLAB (Mathworks, Inc. Natick, MA). Contour sets were evaluated by two experienced reviewers (experience defined by >2 years of clinical work including contour review). 29 cases were determined to be error-free; those with any kind of contouring error (~25% of the total cases reviewed) were excluded from this study).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify system and method for contouring a set of medical images taught by Piper incorporate the teachings of Altman, and applying the framework for automated contour quality assurance in radiation therapy taught by Altman to provide the validation and quality assurance for contouring human anatomy within the medical images. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify Piper according to the relied-upon teachings of Altman to obtain the invention as specified in claim.
Regarding claim 38, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses further comprising editing the one or more contour in response to the feedback (FIG. 4; paragraph [0031], if the automatically generated contour is accepted, then the method proceeds to step 412. Otherwise, if the user chooses to modify the automatically generated target contour, then the contour is manually edited at step 414 before the method proceeds to step 412 ...).
Regarding claim 39, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses further comprising repeating until the structure on the medical image has been fully contoured (FIG. 4; paragraph [0031], if the automatically generated contour is accepted, then the method proceeds to step 412. Otherwise, if the user chooses to modify the automatically generated target contour, then the contour is manually edited at step 414 before the method proceeds to step 412. The dotted line between steps 414 and 404 signifies that if a target contour is manually edited, then during the next iteration of the contouring method (if any), the manually edited contour may be used as the source contour for the next target image);
automatically determining if at least one of the one or more contours conforms to guidelines specific to the structure (Paragraph [0030], in step 404, a target image is identified for contouring based on the source image and source contour. In step 406, a deformation algorithm is applied to the source and target images to generate deformation field data that indicates how one or more objects within the images have changed from the source image to the target image. The deformation field data is then applied to the source contour data in step 408 to automatically generate contour data for the target image by transforming the source contour to match the changes from the source image to the target image); and
In additional, see claim 37, Altman discloses providing feedback about a quality of the one or more contours (Page 5201; Figure 1. Schematic of the automatic contour QA methodology, which uses a database of verified contour datasets to describe the evaluation metrics and decision criteria (left side). New contours are input and evaluated as described on the right side of the diagram. A feedback loop shows how contours validated as accurate by the system can be added to the database and used for further evaluation of additional contour data. See text for more details).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify system and method for contouring a set of medical images taught by Piper incorporate the teachings of Altman, and applying the framework for automated contour quality assurance in radiation therapy taught by Altman to provide the validation and quality assurance for contouring human anatomy within the medical images. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify Piper according to the relied-upon teachings of Altman to obtain the invention as specified in claim.
Regarding claim 40, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses wherein the one or more contour is generated manually or semi-automatically (Paragraph [0048], deformable registration algorithms, such as the deformation algorithm utilized by the systems and methods described above ... A more specific optimization allows the system to be configured for a specific physician, institution, treatment site, image modality, camera or any combination thereof by accepting a set of manually defined contours and medical images as a training set and a contour accuracy metric for scoring the automatically or semi-automatically generated contour results ...).
Regarding claim 41, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses further comprising outputting the one or more contour of the contoured structure of the medical image (FIG. 4; paragraph [0032], if the contouring is complete, then the method proceeds to step 416 at which point a set of contoured images has been generated. In additional, FIG. 3 shows display device 324; paragraph [0029], once the target contour 314 is generated, it may be displayed as an overlay on the target image for the user to review and possibly edit).
Regarding claim 42, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses further comprising:
displaying the one or more contours on the medical image before a user starts to generate a manual contour for the medical image (FIG. 3 shows display device 324; paragraph [0029], once the target contour 314 is generated, it may be displayed as an overlay on the target image for the user to review and possibly edit).
Regarding claim 43, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses further comprising:
displaying the one or more contours on the medical image (FIG. 1; paragraph [0012], an example system 100 for contouring a set of medical images ...; paragraph [0013], the plurality of medical images are loaded into the image database 108 for contouring ...; paragraph [0020], the user could start with a contoured source slice in the middle of the set of images 108 ...; paragraph [0014], the target contour data 118 is then stored in the contour database 110 and may be overlaid onto the target image by the image rendering engine 106) before the automatically determining if at least one of the one or more contours conforms to the guidelines specific to the structure (Paragraph [0014], the image deformation engine 102 receives the source image and the target image from the image database 108 and applies a deformation algorithm to the source and target images to generate deformation field data 116 that indicates how one or more objects within the image have changed from the source to the target. The contour transformation engine 104 then applies the deformation field data 116 to the source contour data 114 to create target contour data 118).
Regarding claim 47, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses wherein the guidelines refer to one or more of:
anatomical boundaries between structures; other anatomical structures; other anatomical features (Paragraph [0002], Medical images, such CT (computed tomography), MR (magnetic resonance), US (ultrasound), or PET (positron emission tomography) scans, are regularly contoured to identify certain pieces of anatomy within the image).
Regarding claim 48, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 47), and Piper discloses wherein a reference anatomy on the medical image is identified using at least one of:
atlas-based auto-contouring; machine learning methods; algorithmic approaches, wherein a comparison of a relative position of the one or more contour to the reference anatomy in the medical image is used to automatically determining if at least one of the one or more contours conforms to the guidelines (Paragraph [0057], in one alternative embodiment an atlas-based segmentation tool may be integrated with the systems and methods described above, where the atlas-based segmentation tool deformably maps images and contours to an image set in question. The atlas may also include information relating to a relationship of a particular piece of anatomy from one slice to the next. This information, along with a mapping of the atlas anatomy to the patient anatomy, may be integrated into the deformation algorithm in order to bias the deformation towards the atlas-indicated formation. For instance, if the slice-to-slice deformation in the atlas-patient shrinks the contour at the bottom of a structure, constraints to the deformation may indicate that the contour at the bottom of the same structure in the current patient should also be shrunk by the slice-to-slice deformation).
Regarding claim 49, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses wherein the medical image is a CT scan, a CBCT scan, a PET scan, a SPECT scan or an MRI scan (Paragraph [0002], Medical images, such CT (computed tomography), MR (magnetic resonance), US (ultrasound), or PET (positron emission tomography) scans, are regularly contoured to identify certain pieces of anatomy within the image).
Regarding claim 50, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37).
However, Piper doses not specifically disclose wherein the feedback is natural language or human interpretable feedback, and wherein the feedback is provided as one of more of: a report by annotation of the contours to indicate the type of error with the contour, wherein the annotation is one or more of: symbolic annotations, textual annotations linked to a line; line weighting change, line colour change, line style change, shading/colouring around the line; shading colouring within an infringing portion of the one or more contours.
In additional, Altman discloses wherein the feedback is natural language or human interpretable feedback, and wherein the feedback is provided as one of more of: a report by annotation of the contours to indicate the type of error with the contour (Page 5204; Table 3. Summary of results from the pilot study. Errors are distributed by structures and the numbers of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) are reported, as with sensitivity (TP/(TP + FN)) and specificity (TN/(TN + FP)) Errors are also presented broken down by type ...), wherein the annotation is one or more of: symbolic annotations, textual annotations linked to a line; line weighting change, line colour change, line style change, shading/colouring around the line; shading colouring within an infringing portion of the one or more contours (Page 5204; Metric computation and evaluation criteria analysis were performed on Pinnacle-exported DICOM data in MATLAB (Mathworks, Inc. Natick, MA). Contour sets were evaluated by two experienced reviewers (experience defined by >2 years of clinical work including contour review). 29 cases were determined to be error-free; those with any kind of contouring error (~25% of the total cases reviewed) were excluded from this study).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify system and method for contouring a set of medical images taught by Piper incorporate the teachings of Altman, and applying the framework for automated contour quality assurance in radiation therapy taught by Altman to provide the validation and quality assurance for contouring human anatomy within the medical images. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify Piper according to the relied-upon teachings of Altman to obtain the invention as specified in claim.
Regarding claim 51, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses wherein a user can select the guidelines (FIG. 1; paragraph [0018], a semi-automatic contouring tool that receives initial contour data 114 for one of the image slices in the set of images 108 and then automatically contours the remaining images slices in the set 108 ...; paragraph [0020], the user could start with a contoured source slice in the middle of the set of images 108, perform automatic contours in a first direction, and then return to the initial contoured source image and perform automatic contours in the other direction).
Regarding claim 52, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses wherein the guidelines to be provided are automatically determined according to the structure being contoured (FIG. 4; paragraph [0030], in step 404, a target image is identified for contouring based on the source image and source contour. In step 406, a deformation algorithm is applied to the source and target images to generate deformation field data that indicates how one or more objects within the images have changed from the source image to the target image. The deformation field data is then applied to the source contour data in step 408 to automatically generate contour data for the target image by transforming the source contour to match the changes from the source image to the target image).
Regarding claim 53, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37), and Piper discloses wherein automatically determining if the one or more contours conforms to the guidelines is determined based in part from a database of previously contoured images (FIG. 3; paragraph [0027], the source contour data 312 may be received from a set of previously generated contour data stored in the contour database 316); wherein the database includes previous feedback on whether and/or how the previously contoured images varied from the guidelines (Paragraphs [0026]-[0027], Identified source and target images from the image database 308 are received by the image deformation engine 310, which applies a deformation algorithm to the images to generate deformation field data, as described above. The deformation field data is supplied to the contour transformation engine 306, which applies the deformation field data to a source contour 312, as described above, to automatically generate target contour data 314 .... an initial source image may be manually contoured using the manual contouring software 302 and stored in the contour database 316 for use as the source contour data 312 for the initial target image).
Regarding claim 56, Piper discloses a system for contouring medical images, comprising:
a processor (Paragraphs [0054]-[0055], FIG. 16 is a block diagram of hardware 1310 which may be used to implement the various embodiments described herein. The hardware 1310 may be a personal computer system or server system ... Residing within computer 1320 is a main processor 1324 which is comprised of a host central processing unit 1326 (CPU)); and
a memory coupled to the processor (Paragraph [0055], the main processor 1324 operates in conjunction with a memory subsystem 1330. The memory subsystem 1330 is comprised of the main memory 1329), the memory comprising instructions (Paragraph [0055], software applications 1327 may be loaded from, for example, disk 1328 (or other device), into main memory 1329 from which the software application 1327 may be run on the host CPU 1326), which when executed cause the processor to:
load a medical image and one of more contours (Paragraph [0030], FIG. 4 is a flow diagram of another example method 400 for contouring a set of medical images. In step 402, the method is initialized with an initial source image with associated source contour data ...; paragraph [0002], medical images, such CT (computed tomography), MR (magnetic resonance), US (ultrasound), or PET (positron emission tomography) scans, are regularly contoured to identify certain pieces of anatomy within the image. For example, a radiologist or oncologist may contour a medical image to identify a tumor within the image; paragraph [0032], in other embodiments, a non-consecutive slice may be contoured by direct deformation from the previously contoured target image or by computing and concatenating deformations pairwise through a chain of consecutive slices ... For example: FIG. 1; paragraph [0012], an example system 100 for contouring a set of medical images ...; paragraph [0013], the plurality of medical images are loaded into the image database 108 for contouring ...; paragraph [0020], the user could start with a contoured source slice in the middle of the set of images 108 ...) associated with a structure represented in the medical image (Paragraph [0002], medical images, such CT (computed tomography), MR (magnetic resonance), US (ultrasound), or PET (positron emission tomography) scans, are regularly contoured to identify certain pieces of anatomy within the image. For example, a radiologist or oncologist may contour a medical image to identify a tumor within the image);
automatically determine if at least one of the one or more contours conforms to guidelines specific to the structure (Paragraph [0030], in step 404, a target image is identified for contouring based on the source image and source contour. In step 406, a deformation algorithm is applied to the source and target images to generate deformation field data that indicates how one or more objects within the images have changed from the source image to the target image. The deformation field data is then applied to the source contour data in step 408 to automatically generate contour data for the target image by transforming the source contour to match the changes from the source image to the target image); and
in response to a determination that the at least one of the one or more contours conforms to the guidelines (Paragraph [0031], at decision step 410, a user input is received to either accept or modify the automatically generated target contour),
wherein the contouring guidelines refer to one or more of: anatomical boundaries between structures, other anatomical structures, other anatomical features (Paragraph [0002], ... a radiologist or oncologist may contour a medical image to identify a tumor within the image. Software tools are available to assist in this type of "manual" contouring, in which the physician uses the software to create the contour by tracing the boundary of the object or objects within the image).
However, Piper doses not specifically disclose providing feedback about a quality of the one or more contours, and
wherein the feedback is provided by annotation of the one or more contours to indicate a type of error with the contour.
In additional, Altman discloses (Abstract, contouring of targets and normal tissues is one of the largest sources of variability in radiation therapy treatment plans. Contours thus require a time intensive and error-prone quality assurance (QA) evaluation, limitations which also impair the facilitation of adaptive radiotherapy (ART). Here, an automated system for contour QA is developed using historical data (the ‘knowledge base’) ...) providing feedback about a quality of the one or more contours (Page 5201; Figure 1. Schematic of the automatic contour QA methodology, which uses a database of verified contour datasets to describe the evaluation metrics and decision criteria (left side). New contours are input and evaluated as described on the right side of the diagram. A feedback loop shows how contours validated as accurate by the system can be added to the database and used for further evaluation of additional contour data. See text for more details), and
wherein the feedback is provided by annotation of the one or more contours to indicate a type of error with the contour (Page 5204; Table 3. Summary of results from the pilot study. Errors are distributed by structures and the numbers of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) are reported, as with sensitivity (TP/(TP + FN)) and specificity (TN/(TN + FP)) Errors are also presented broken down by type ... Metric computation and evaluation criteria analysis were performed on Pinnacle-exported DICOM data in MATLAB (Mathworks, Inc. Natick, MA). Contour sets were evaluated by two experienced reviewers (experience defined by >2 years of clinical work including contour review). 29 cases were determined to be error-free; those with any kind of contouring error (~25% of the total cases reviewed) were excluded from this study).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify system and method for contouring a set of medical images taught by Piper incorporate the teachings of Altman, and applying the framework for automated contour quality assurance in radiation therapy taught by Altman to provide the validation and quality assurance for contouring human anatomy within the medical images. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify Piper according to the relied-upon teachings of Altman to obtain the invention as specified in claim.
Regarding claim 57, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 56), and Piper discloses further comprising a display, wherein the instructions when executed by the processor further cause the processor to display the one or more contours of the contoured structure of the medical image (FIG. 3 shows display device 324; paragraph [0029], once the target contour 314 is generated, it may be displayed as an overlay on the target image for the user to review and possibly edit).
Regarding claim 58, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 56), and Piper discloses wherein the instructions when executed by the processor further cause the processor to repeat, until the structure on the medical image has been fully contoured (FIG. 4; paragraph [0031], if the automatically generated contour is accepted, then the method proceeds to step 412. Otherwise, if the user chooses to modify the automatically generated target contour, then the contour is manually edited at step 414 before the method proceeds to step 412. The dotted line between steps 414 and 404 signifies that if a target contour is manually edited, then during the next iteration of the contouring method (if any), the manually edited contour may be used as the source contour for the next target image):
automatically determining if at least one of the one or more contours conforms to guidelines specific to the structure (Paragraph [0030], in step 404, a target image is identified for contouring based on the source image and source contour. In step 406, a deformation algorithm is applied to the source and target images to generate deformation field data that indicates how one or more objects within the images have changed from the source image to the target image. The deformation field data is then applied to the source contour data in step 408 to automatically generate contour data for the target image by transforming the source contour to match the changes from the source image to the target image); and
In additional, see claim 37, Altman discloses providing feedback about a quality of the one or more contours (Page 5201; Figure 1. Schematic of the automatic contour QA methodology, which uses a database of verified contour datasets to describe the evaluation metrics and decision criteria (left side). New contours are input and evaluated as described on the right side of the diagram. A feedback loop shows how contours validated as accurate by the system can be added to the database and used for further evaluation of additional contour data. See text for more details).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify system and method for contouring a set of medical images taught by Piper incorporate the teachings of Altman, and applying the framework for automated contour quality assurance in radiation therapy taught by Altman to provide the validation and quality assurance for contouring human anatomy within the medical images. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify Piper according to the relied-upon teachings of Altman to obtain the invention as specified in claim.
Claim 44 is rejected under 35 U.S.C. 103 as being unpatentable over Piper (U.S. Patent Application Publication 2011/0268330 A1) in view of Altman et al (“ALTMAN et al., "A Framework for Automated Contour Quality Assurance in Radiation Therapy Including Adaptive Techniques," Physics in Medicine & Biology, Volume 60, Pages 5199-5209. June 2015) in view of Hu et al (U.S. Patent Application Publication 2008/0030497A1).
Regarding claim 44, the combination of Piper in view of Altman discloses everything claimed as applied above (see claim 37).
However, Piper doses not specifically disclose further comprising determining at least one infringement zone from the guidelines specific to the structure, wherein the at least one infringement zone is used to determine a region of the medical image where the one or more contours should not be placed.
In additional, Hu discloses (Paragraph [0015], the invention provides an image segmentation method that can create n-dimensional, for example 3D, digital models of objects faster, and more accurately, than prior techniques ... While the fast image segmentation techniques discussed herein can be deployed in any number of settings, for exemplary purposes the following description focuses on segmentation of anatomical structures such as bones, organs, soft tissue, muscle, and blood vessels from medical images such as CT, MR, PET, OCT, ultrasound images, etc. ...) further comprising determining at least one infringement zone from the guidelines specific to the structure (Paragraph [0018], as illustrated in FIG. 1, and described in more detail below, a method is disclosed that uses a graph cuts method to approximately identify image elements or structures in an image data set that correspond to a particular structure such as a bone, and then refines the identification of the particular structure using a level set method; paragraphs [0029]-[0032], an exemplary speed function for the level set module used in a current embodiment of the present invention is ...;paragraph [0033], when it is desired to do a segmentation for more than one bone in an image set, the level set method is run separately for each bone. The labels of other bones in the results of graph cuts method are set as a "forbidden region" (e.g., by setting the speed equal to zero) ... Thus, "forbidden region" of bone can be interpreted as infringement zone and “setting the speed equal to zero” can be interpreted as guidelines), wherein the at least one infringement zone is used to determine a region of the medical image where the one or more contours should not be placed (Paragraph [0015], while the fast image segmentation techniques discussed herein can be deployed in any number of settings, for exemplary purposes the following description focuses on segmentation of anatomical structures such as bones, organs, soft tissue, muscle, and blood vessels from medical images such as CT, MR, PET, OCT, ultrasound images, etc. ...; paragraph [0033], the labels of other bones in the results of graph cuts method are set as a "forbidden region" (e.g., by setting the speed equal to zero) when the contour of one bone is evolving, in order to prevent the final contours of bones from overlapping one another ...).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify system and method for contouring a set of medical images taught by Piper in view of Altman incorporate the teachings of Hu, and applying the image segmentation method taught by Hu to set a "forbidden region" by setting the level for contouring each bone in order to prevent the final contours of bones from overlapping one another. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify Piper in view of Altman according to the relied-upon teachings of Hu to obtain the invention as specified in claim.
Allowable Subject Matter
Claims 45-46 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/XILIN GUO/Primary Examiner, Art Unit 2616
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