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 .
DETAILED ACTION
Claims 1 -19 are pending in this application. Claim 1 is independent.
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 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 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.
Claim(s) 1 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tan, Yongqiang (US-20190355117-A1, hereinafter simply referred to as Tan) in view of Liu, Siqi (US-20210327054-A1, hereinafter simply referred to as Liu).
Regarding independent claim 1, Tan teaches:
A computer-implemented method (e.g., FIG. 2 of Tan) for determining the size of a lesion (e.g., FIG. 1A, #134a/134b of Tan) from a scan of at least a part of a patient (e.g., FIG. 1A, #132a of Tan) (See at least Tan, ¶ [0041–0042, 0086]; FIGS. 1, 2), the method comprising: (a) obtaining at least one image based on a scan of at least a part of a patient's anatomy (See at least Tan, ¶ [0041–0042, 0086]; FIGS. 1, 2); (b) applying a plurality of different segmentation techniques (e.g., segmentation techniques of Tan) to the at least one image to generate a plurality of segmentation masks (e.g., various ROI mask values of Tan), wherein each segmentation technique is configured to generate a segmentation mask to identify an area within the at least one image comprising a lesion (e.g., the target tissue (such as a an organ or tumor) of Tan) (See at least Tan, ¶ [0051, 0071, 0074]; FIGS. 1, 2).
Tan teaches the subject matter of the claimed inventive concept as expressed in the rejections above, and further teaches a plurality of constraints/ rules that need to be satisfied for segmentation.
But, Tan does not expressly disclose the concept of (c) generating a final segmentation mask to identify a final area within the at least one image comprising the lesion, based on combining the plurality of segmentation masks to satisfy at least one rule; and (d) segmenting the at least one image using the final segmentation mask to define the area of a lesion within the at least one image.
Nevertheless, Liu teaches the concept of (c) generating a final segmentation mask (e.g., synthesized segmentation mask of Tan) to identify a final area within the at least one image comprising the lesion, based on combining the plurality of segmentation masks to satisfy at least one rule (e.g., rule of Tan) (See at least Liu, ¶ [0040–0041, 0068]; FIGS. 1 – 6); and (d) segmenting the at least one image using the final segmentation mask to define the area of a lesion within the at least one image (See at least Liu, ¶ [0040–0041, 0068]; FIGS. 1 – 6).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use and apply the known technique of generating a final segmentation mask to identify a final area within the at least one image comprising the lesion, based on combining the plurality of segmentation masks to satisfy at least one rule; and (d) segmenting the at least one image using the final segmentation mask to define the area of a lesion within the at least one image as disclosed in the device of Liu to modify and improve the known and similar device of Tan for the desirable and advantageous purpose of providing for realistic synthesized medical images of abnormality patterns that may be utilized for training machine learning based systems for, e.g., assessment of a disease, as discussed in Liu (See ¶ [0031]); thereby, achieving the predictable result of improving the overall efficiency and speed of the system with a reasonable expectation of success while enabling others skilled in the art to best utilize the invention along with various implementations and modifications as are suited to the particular use contemplated.
Regarding dependent claim 2, Tan modified by Liu above teaches:
wherein generating the final segmentation mask (e.g., synthesized segmentation mask of Tan) comprises combining the plurality of segmentation masks to satisfy a set of rules (e.g., plurality of constraints/ rules of Tan & Liu) (See at least Liu, ¶ [0040–0041, 0068]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0051, 0071, 0074]; FIGS. 1–4).
Regarding dependent claim 3, Tan modified by Liu above teaches:
wherein generating the final segmentation mask (e.g., synthesized segmentation mask of Tan) comprises combining the plurality of segmentation masks using a genetic algorithm (e.g., genetic algorithm of Liu) to satisfy the at least one rule (e.g., plurality of constraints/ rules of Tan & Liu) (See at least Liu, ¶ [0040–0041, 0068]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0051, 0071, 0074]; FIGS. 1–4).
Regarding dependent claim 4, Tan modified by Liu above teaches:
obtaining a bounding box (e.g., bounding box of Liu/ VOI of Tan) for the at least one image, wherein the bounding box defines a perimeter of an area of the at least one image which comprises the lesion (See at least Liu, ¶ [0059]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0087, 0118]; FIGS. 1–4), and wherein the plurality of segmentation masks are applied to the area defined by the bounding box (See at least Liu, ¶ [0059]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0087, 0118]; FIGS. 1–4).
Regarding dependent claim 5, Tan modified by Liu above teaches:
wherein the at least one rule comprises a boundary constraint configured to ensure that the final segmentation mask is configured such that the final area defined by the final segmentation mask does not contact or intersect the perimeter of the bounding box (See at least Tan, ¶ [0069]; FIGS. 1–4. Also, see at least Liu, ¶ [0047]; FIGS. 1–6).
Regarding dependent claim 6, Tan modified by Liu above teaches:
wherein the at least one rule comprises a centroid proximity constraint configured to ensure that the centroid of a lesion segmented from the final mask is maintained within a defined distance relative to the centroid of the lesion segmented from at least one of the plurality of segmentation masks (See at least Tan, ¶ [0069, 0074]; FIGS. 1–4. Also, see at least Liu, ¶ [0047]; FIGS. 1–6).
Regarding dependent claim 7, Tan modified by Liu above teaches:
wherein obtaining the at least one image comprises obtaining a plurality of images based on a scan of at least a part of a patient's anatomy, each image corresponding to a cross-sectional slice from the scan (See at least Tan, ¶ [0023, 0042–0043]; FIGS. 1–4. Also, see at least Liu, ¶ [0063]; FIGS. 1 – 6); and repeating steps (b) to (d) for each of the plurality of images (See at least Tan, ¶ [0023, 0042–0043]; FIGS. 1–4. Also, see at least Liu, ¶ [0063]; FIGS. 1 – 6).
Regarding dependent claim 8, Tan modified by Liu above teaches:
wherein generating the final segmentation mask comprises obtaining a similarity score (e.g., via Dice similarity coefficient of Liu) configured to measure the similarity between the final segmentation mask and at least one of the plurality of segmentation masks; and wherein the at least one rule comprises a similarity score constraint configured to ensure that the obtained similarity score is above a predetermined threshold or within a predetermined range (See at least Liu, ¶ [0068, 0071]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0077, 0081]; FIGS. 1–4).
Regarding dependent claim 9, Tan modified by Liu above teaches:
wherein the obtained at least one image is based on a scan of at least a part of a patient's lung (See at least Liu, ¶ [0071]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0040, 0053]; FIGS. 1–4).
Regarding dependent claim 10, Tan modified by Liu above teaches:
wherein a first segmentation technique of the plurality of segmentation techniques is configured to generate a first segmentation mask to delineate parenchyma (e.g., parenchyma of Liu & Tan) from other lung tissue, for example wherein the first mask is a Kmeans mask (e.g., the trained machine learning based network can be based on k-means of Liu) (See at least Liu, ¶ [0071, 0079]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0054, 0090, 0115]; FIGS. 1–4); and wherein the at least one rule comprises a mask containment constraint, wherein the mask containment constraint is configured to ensure that the final segmentation mask must be confined within the area of the first mask (See at least Liu, ¶ [0079]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0069, 0115]; FIGS. 1–4).
Regarding dependent claim 11, Tan modified by Liu above teaches:
wherein the plurality of segmentation masks comprises three different segmentation masks (See at least Liu, ¶ [0037]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0054]; FIGS. 1–4).
Regarding dependent claim 12, Tan modified by Liu above teaches:
wherein the at least one rule comprises a centroid proximity constraint configured to ensure that the centroid of a lesion segmented from a second image is maintained within a defined distance relative to the centroid of the lesion segmented from a first image (See at least Tan, ¶ [0078]; FIGS. 1–4), wherein the first image and the second image are adjacent cross-sectional slices from the scan (See at least Tan, ¶ [0042–0043]; FIGS. 1–4).
Regarding dependent claim 13, Tan modified by Liu above teaches:
wherein the at least one rule comprises an area continuity constraint configured to minimize area penalties, wherein area penalties are incurred for segmentation masks wherein the area of a lesion segmented from a second image is greater than the area of the lesion segmented from a first image (See at least Tan, ¶ [0078]; FIGS. 1–4), wherein the first image and the second image are adjacent cross-sectional slices from the scan, and wherein the first image is obtained closer to the centroid of the lesion than the second image (See at least Tan, ¶ [0042–0043]; FIGS. 1–4).
Regarding dependent claim 14, Tan modified by Liu above teaches:
wherein the at least one rule comprises a shape integrity constraint configured to ensure that irregular boundaries representing outgrowths in shape of a lesion segmented from an image are minimized (See at least Tan, ¶ [0074, 0076]; FIGS. 1–4), such that the shape integrity constraint is configured to promote smooth and regular lesion shapes segmented from an image (See at least Tan, ¶ [0074, 0076, 0107]; FIGS. 1–4).
Regarding dependent claim 15, Tan modified by Liu above teaches:
determining the area of the lesion based on the segmented area of the at least one image (See at least Liu, ¶ [0040–0041, 0068]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0051, 0071, 0074]; FIGS. 1, 2).
Regarding dependent claim 16, Tan modified by Liu above teaches:
wherein obtaining the at least one image based on a scan of at least a part of a patient's lung comprises obtaining a plurality of images, each image corresponding to a cross-sectional slice from the scan (See at least Tan, ¶ [0023, 0042–0043]; FIGS. 1–4. Also, see at least Liu, ¶ [0063]; FIGS. 1 – 6); and the method further comprising determining the volume of the lesion based on the segmented area of the lesion in each of the plurality of segmented images (See at least Tan, ¶ [0042–0043, 0057]; FIGS. 1–4).
Regarding dependent claim 16, Tan modified by Liu above teaches:
wherein obtaining the at least one image based on a scan of at least a part of a patient's lung comprises obtaining a plurality of images, each image corresponding to a cross-sectional slice from the scan (See at least Tan, ¶ [0023, 0042–0043]; FIGS. 1–4. Also, see at least Liu, ¶ [0063]; FIGS. 1 – 6); and the method further comprising determining the volume of the lesion based on the segmented area of the lesion in each of the plurality of segmented images (See at least Tan, ¶ [0042–0043, 0057]; FIGS. 1–4).
Regarding dependent claim 17, Tan modified by Liu above teaches:
generating a three-dimensional model of the lesion based on the plurality of segmented images (See at least Tan, ¶ [0023, 0042–0043, 0115]; FIGS. 1–4. Also, see at least Liu, ¶ [0058]; FIGS. 1 – 6); and optionally determining the volume of the lesion based on the three-dimensional model of the lesion (See at least Tan, ¶ [0023, 0042–0043, 0115]; FIGS. 1–4. Also, see at least Liu, ¶ [0058]; FIGS. 1 – 6).
Regarding dependent claim 18, Tan modified by Liu above teaches:
applying a weighting to each pixel within the segmented area of the at least one segmented image (See at least Tan, ¶ [0074]; FIGS. 1–4. Also, see at least Liu, ¶ [0053]; FIGS. 1 – 6), wherein the weighting is based on the relative intensity of each pixel within the segmented area (See at least Tan, ¶ [0074]; FIGS. 1–4. Also, see at least Liu, ¶ [0053]; FIGS. 1 – 6); and determining an indication of mass of the lesion based on the weightings applied to each pixel within the segmented area of the at least one segmented image (See at least Tan, ¶ [0074]; FIGS. 1–4. Also, see at least Liu, ¶ [0053]; FIGS. 1 – 6).
Regarding dependent claim 19, Tan modified by Liu above teaches:
A computer readable non-transitory storage medium comprising a program for a computer configured to cause a processor to perform the method of claim 1 (See at least Liu, ¶ [0068, 0071]; FIGS. 1 – 6. Also, see at least Tan, ¶ [0077, 0081]; FIGS. 1–4).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: See the Notice of References Cited (PTO–892)
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/IDOWU O OSIFADE/Primary Examiner, Art Unit 2675
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