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 .
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 9 and 13 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the term “basic” which is a relative and/or subjective term that renders the claim indefinite. The term “basic” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Thereby it is unclear what “basic elements” are. For example, are they foreground and background elements? are they particular organs?
Claim 13 recites the limitation "wherein selecting the region" in line 1. There is insufficient antecedent basis for this limitation in the claim. It’s unclear if this is referring to the, “selecting an element of the object” step or the “determining a segmentation region” step of claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 9, and 13-20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Gazit (US 20160300343 A1).
Regarding Claim 1, representative of Claims 17 and 20, Gazit teaches a method for segmenting an object in a source image, comprising:
providing a source image showing details of the object ([abstract]: A method of segmenting a target organ of the body in medical images);
selecting an element of the object ([0122] FIG. 5B shows a flow chart 508 for segmenting a target organ in a test image, [0128]: The procedure starts at 602, when a test image is selected. At 604, the first target organ is selected);
determining a segmentation region in the source image comprising the selected element ([0123] At 510, a bounding region for the target organ is found in the medical image);
preparing a segmentation mask by segmenting the determined segmentation region, wherein at least the selected element or its components are segmented ([0124] At 514, a rough segmentation of the target organ is found in the bounding region, [0125] At 516, once the rough segmentation is found, the probabilistic atlas is optionally used to find a relatively high probability region of the rough segmentation, [0126] At 518, a seed comprising one or more seed voxels is optionally found in the relatively high probability region of the rough segmentation, [0127] At 520, the seed found at 518 is used to grow a more accurate segmentation, using a region-growing algorithm); and
outputting the segmentation mask ([0205]: Segmentation module 1208 uses training data 1206 to segment test images 1210, at least the ones selected by selection module 1212, and outputs the result as segmentation masks).
Regarding Claim 2, Gazit teaches the method according to claim 1. In addition, Gazit teaches wherein the segmentation region is a volume-representation of the selected element or a bounding box around the selected element ([0123] At 510, a bounding region for the target organ is found in the medical image, [0098]: The bounding region is, for example, a rectangular box).
Regarding Claim 3, Gazit teaches the method according to claim 1. In addition, Gazit teaches wherein the segmentation region is determined by
identifying a region in the source image ([0129]: any known method is used for finding a preliminary segmentation for the target organ…Known methods of finding a preliminary segmentation or a bounding region of a target organ in a medical image include),
identifying a respective region in a model of the object and transferring the respective region into the source image ([0129]: any known method is used for finding a preliminary segmentation for the target organ… [0131] 2) Registration-based methods, in which a medical image is registered to an image in an atlas), and
selecting a respective region in the source image while selecting the element of the object ([0129]: the bounding region is defined the same way as in the training images, for example as the smallest rectangular box, with principle axes aligned with the principle body axes, that includes all of the target organ according to the preliminary segmentation).
Regarding Claim 9, Gazit teaches the method according to claim 1. In addition, Gazit teaches wherein the segmentation region is determined by pre-segmenting the object in the source image with a pre-segmentation routine into basic elements and determining the segmentation region based on this pre- segmentation ([0129]: the bounding region is defined the same way as in the training images, for example as the smallest rectangular box, with principle axes aligned with the principle body axes, that includes all of the target organ according to the preliminary segmentation).
Regarding Claim 13, Gazit teaches the method according to claim 1. In addition, Gazit teaches wherein selecting the region in the source image comprises selecting a spheric region, a rectangular box ([0129]: the bounding region is defined the same way as in the training images, for example as the smallest rectangular box, with principle axes aligned with the principle body axes, that includes all of the target organ according to the preliminary segmentation) or scribble.
Regarding Claim 14, Gazit teaches the method according to claim 1. In addition, Gazit teaches wherein outputting the segmentation mask comprises:
visualizing the segmentation mask over the source image ([0083]: segmentation mask is then displayed to a user…the segmentation mask is optionally combined with the image, and rendered as a 2-dimensional or 3-dimensional display), and
altering or adjusting the segmentation mask based on the source image ([0087]: some embodiments of the invention concerns segmenting two or more organs or other objects in a medical image … identifying an overlapping region of two or more of the masks, and assigning at least some voxels in the overlap region to only one of the organs…optionally, if the mask for one of the organs almost completely surrounds the other organ, then the overlap voxels are assigned to the surrounding organ, and the surrounded organ is deleted) or performing an automated connected component analysis in order to cluster regions in the segmentation mask.
Regarding Claim 15, Gazit teaches the method according to claim 1. In addition, Gazit teaches wherein the source image is a medical image ([0072]: segmenting an organ or other structure in a medical image).
Regarding Claim 16, Gazit teaches the method according to claim 15. In addition, Gazit teaches wherein the source image is a computed tomography (CT)-image ([0072]: tests of the method made by the inventors, all involve three-dimensional CT images, the method can also be used with medical images produced by other modalities), magnetic resonance tomography (MRT)-image, positron emission tomography (PET)-image, Ultrasound-image, X-ray image or tomosynthesis image.
Regarding Claim 18, Gazit teaches the device according to claim 17. In addition, Gazit teaches comprising a model unit with information about a model of the object and elements of the object surrounded by regions ([0129]: any known method is used for finding a preliminary segmentation for the target organ, and the bounding region is defined the same way as in the training images, for example as the smallest rectangular box…that includes all of the target organ according to the preliminary segmentation…Known methods of finding a preliminary segmentation…medical image include, [0131]: Registration-based methods, in which a medical image is registered to an image in an atlas that covers a large field of view, such as the entire abdomen, or the entire body), wherein the device determines a segmentation region by identifying an element of the model and a region of the element as the segmentation region ([0131]: Registration-based methods, in which a medical image is registered to an image in an atlas that covers a large field of view, such as the entire abdomen, or the entire body, [0129]: bounding region is defined the same way as in the training images, for example as the smallest rectangular box…that includes all of the target organ according to the preliminary segmentation. Examiner interpreting preliminary segmentation of the target organ using the atlas as an element of the model and interpreting a region of the element as the bounding box encompassing the preliminary segmentation).
Regarding Claim 19, Gazit teaches the device according to claim 18. In addition, Gazit teaches wherein the information comprises a list of elements and coordinates of their regions ([0131] 2) Registration-based methods, in which a medical image is registered to an image in an atlas that covers a large field of view, such as the entire abdomen, or the entire body. An example of such a method is described in Hyunjin Park, et al, “Construction of an Abdominal Probabilistic Atlas and its Application in Segmentation,” IEEE Transactions on Medical Imaging, Vol. 22, No. 4, April 2003, 483-492. Examiner notes the atlas has probabilities of voxels belonging to one or more organs).
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, 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) 4-8 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Gazit (US 20160300343 A1) in view of Sharp (US 20110228997 A1).
Regarding Claim 4, Gazit teaches the method according to claim 3. However, Gazit does not explicitly teach the remaining limitations of Claim 4. Sharp teaches comprising:
identifying the selected element and at least one coordinate of a position of the selected element in the object and determining the coordinate from a database ([0029]: user makes a user action at the user interface which is received 204. The user action specifies at least one of the organ names. For example, the user action may be a single click on a button for that organ label, [0031]: a bounding box (or bounding region) may be determined using an organ label and examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label. Examiner interpreting the coordinate as included in determining a bounding box encompassing the organ/selected element, [0053]: communication interface 904 can also be used to communicate with one or more external computing devices, and with databases. Examiner notes storing training data in a database is well known to one of ordinary skill in the art. Examiner notes training data according to Sharp are images with known bounding boxes for a particular organ label); and
determining the segmentation region from a model of the object and the coordinate and combining the segmentation region with the coordinate of the source image ([0031]: a bounding box (or bounding region) may be determined using an organ label and examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label, [0040]: medical image 616 is delineated by being enclosed within a bounding box).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified the teachings of Gazit to include the teachings of Sharp by substituting the bounding box determination of Gazit by the bounding box determination of Sharp involving selection of an organ/labelling of an organ and retrieval of a bounding box according to the organ. Doing so would provide the predictable result of a bounding box encompassing an organ of interest for which a segmentation can be performed within.
Regarding Claim 5, the Gazit and Sharp combination teaches the method according to claim 4. In addition, Sharp teaches wherein identifying the selected element and the at least one coordinate of the position of the selected element in the object comprises using a selected coordinate or a known coordinate of the selected element in the object or in a model of the object, and retrieving a label for the identified selected element ([0029]: user makes a user action at the user interface which is received 204. The user action specifies at least one of the organ names. For example, the user action may be a single click on a button for that organ label, [0031]: a bounding box (or bounding region) may be determined using an organ label and examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label. Examiner interpreting a known coordinate of the selected element to be part of the bounding boxes from known training data).
Regarding Claim 6, the Gazit and Sharp combination teaches the method according to claim 4. In addition, Sharp teaches further comprising calibrating size, shape, orientation, position or combination thereof of the segmentation region relative to the source image based on picture information of the source image ([0040]: medical image 616 is delineated by being enclosed within a bounding box, [0040]: For each pixel of a two-dimensional image a ray is computed 704 which projects from a specified camera position through a pixel and through the bounding box. Each ray is clipped according to the bounding box 706).
Regarding Claim 7, the Gazit and Sharp combination teaches the method according to claim 4. In addition, Sharp teaches wherein the model comprises a list of elements and coordinates of regions comprising the list of elements ([0031]: examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label, [0024]: if a medical image is labeled with three organ labels, then three organ centers may be estimated one for each organ label. The organ recognition system 102 may be arranged to provide an estimated bounding region or bounding box for each organ it detects. Examiner notes multiple organ labels may exist), wherein the coordinates of regions are chosen as the segmentation region by identifying an element of the list to be a selected element ([0029]: user makes a user action at the user interface which is received 204. The user action specifies at least one of the organ names. For example, the user action may be a single click on a button for that organ label, [0031]: a bounding box (or bounding region) may be determined using an organ label and examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label).
Regarding Claim 8, the Gazit and Sharp combination teaches the method according to claim 7. In addition, Sharp teaches wherein selecting the element is based on a selected coordinate, the element of the list is identified by location of the element in the model ([0031]: a bounding box (or bounding region) may be determined using an organ label and examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label. Examiner interpreting “selecting the element based on a selected coordinate” is done by selecting a particular organ label, of which an organ has a location/coordinate in the training data as represented with the associated bounding box) and the selected coordinate is mapped on the model ([0031]: a bounding box (or bounding region) may be determined using an organ label and examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label. Examiner interpreting the training data of a particular organ has known bounding boxes).
Regarding Claim 12, Gazit teaches the method according to claim 1. However, Gazit does not explicitly teach the remaining limitations of claim 12. Sharp teaches wherein the element is selected by
selecting a position of the element in the source image by pointing on at least a point in the source image,
selecting a region in the source image,
inputting an expression of the element and determining the position of the element in the source image based on prior knowledge or a fast pre-segmentation step,
inputting the expression of the element and determining the segmentation region by using a list of elements and their segmentation regions or a model with given segmentation regions ([0029]: user makes a user action at the user interface which is received 204. The user action specifies at least one of the organ names. For example, the user action may be a single click on a button for that organ label, [0031]: a bounding box (or bounding region) may be determined using an organ label and examples of bounding boxes which were successful for previous medical images (i.e. training data) with the same organ label),
using a predefined position or element in the source image or in the model, or
using information about an examination of the object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified the teachings of Gazit to include the teachings of Sharp by substituting the bounding box determination of Gazit by the bounding box determination of Sharp involving selection of an organ/labelling of an organ and retrieval of a bounding box according to the organ. Doing so would provide the predictable result of a bounding box encompassing an organ of interest for which a segmentation can be performed within.
Claim(s) 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Gazit (US 20160300343 A1) in view of Rusko (US 20210125707 A1).
Regarding Claim 10, Gazit teaches the method according to claim 1. Although Gazit teaches a region growing segmentation within the bounding region, Gazit does not explicitly teach the limitations of Claim 10.
Rusko teaches wherein the segmentation mask is prepared by defining a grid over the segmentation region with a predefined resolution ([0007] Segmentation is the process of assigning labels to individual voxels in the data set of the 3D medical image. Examiner interpreting voxels to makeup the grid), and labeling every grid point during a segmentation procedure in order to obtain labels for the element or for sub-structures of the element ([0007] Segmentation is the process of assigning labels to individual voxels in the data set of the 3D medical image. Automatic segmentation thereby means automated recognition and labeling of human anatomical structures in 2D or 3D digital scans of the human body, [0006] Automated image segmentation of organs)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified the teachings of Gazit to include the teachings of Rusko by substituting the region growing segmentation, for pixel level classification segmentation. Doing so would provide the predictable result of segmentation of an organ.
Regarding Claim 11, the Gazit and Rusko combination teaches the method according to claim 10. In addition, Rusko further comprising removing labels concerning other elements of the object ([0007] Segmentation is the process of assigning labels to individual voxels in the data set of the 3D medical image. Automatic segmentation thereby means automated recognition and labeling of human anatomical structures in 2D or 3D digital scans of the human body, [0006] Automated image segmentation of organs, [0010] There is a special need to segment various organs or particular anatomical structures or parts. [0064]: an output mask can be outputted as a result of the segmentation, Examiner notes segmenting a particular organ/generating segmentation mask would indicate extracting the label of the organ of interest from other labeled voxels).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANICE VAZ whose telephone number is (703)756-4685. The examiner can normally be reached Monday-Friday 9:00-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JANICE E. VAZ/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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