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 statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 3, 8 and 9 are objected to because of the following informalities:
Claim 3 recites the limitation “the mapping function”. Claim 1 does not define the limitation “mapping function”, however, claim 2 defines “a mapping function”. Dependency of claim 3 on claim 1 could be changed to claim 2 to overcome the objection.
Claim 8 recite the limitation “the first channel and the second are part of…”, it should read as “the first channel and the second channel are part of…” (emphasis added with underline).
In Claim 9, change “generate” to “generating”.
Appropriate correction is required.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 2 and 4 – 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zuiderveld et al. (US 20240070904 A1; hereafter referred to as Zuiderveld) in view of Remiszewski et al. (US 20160012591 A1; hereafter referred to as Remiszewski).
Regarding Claim 1, Zuiderveld teaches:
An apparatus, comprising one or more processors and one or more storage devices (Zuiderveld, [0113] “ a program including computer executable instructions for making a processor, computer, or other programmable device execute the operations of the methods”), wherein the apparatus is configured to:
obtain reference sample data indicative of a first image of a sample from a first channel (Zuiderveld, [0017]” the image may be a multiplexed immunofluorescence image having a plurality of channels”; [0030] FIG. 1 shows an example of a multiplexed immunofluorescence (MPX) image; Zuiderveld, [0059] “ FIG. 1 shows an example of a pseudocolor version of a multiplexed immunofluorescence (MPX) image in which a corresponding pseudocolor (e.g., pink, blue, green) is assigned to each of the different channels of the MPX image”; [0105] “At block 1608, a plurality of locations of a first biomarker in the image are obtained. In some instances, the plurality of seed locations are obtained from a first channel of the image”);
obtain threshold data indicative of a plurality of thresholds for performing a distance transformation (Zuiderveld, [0089] “FIG. 11B is a flowchart illustrating an example of a method 1100 for computing distances between locations of a medical image (e.g., an MPX image) that meet first and second criteria… pixels in a blank tile that correspond to image locations which meet a first criterion are marked to produce a marked tile”);
generate, using at least two thresholds of the plurality of thresholds, reference data by applying the distance transformation to the reference sample data (Zuiderveld, [0090] At block 1108, a distance transform array is computed for the marked tile. For each pixel of the marked tile, the distance transform array has a corresponding value that indicates the distance, within the marked tile, from that pixel to the closest marked pixel. At block 1112, the values of the distance transform array that correspond to image locations that meet the second criterion are selected and stored. The second criterion may be, for example, a second selected phenotype (or a second combination of selected phenotypes), possibly further limited to a particular tissue region. In such manner, an instance of method 1100 may be performed (e.g., in parallel) for each tile of interest”; Zuiderveld, [0106] “a first distance transform array for at least a portion of the image that includes the plurality of seed locations is calculated, each value of the first distance transform array corresponding to a respective pixel among the plurality of pixels and indicating a distance from the pixel to a closest among the plurality of locations of the first biomarker”);
obtain sample data indicative of a second image of the sample from a second channel different from the first channel (Zuiderveld, [0105] “the plurality of locations of a first biomarker are obtained from a second channel of the image”); and
generate, based on the reference data and the sample data, structure image data indicative of a structure of the sample (Zuiderveld, [0095] “Referring to FIG. 14, at block 1404, a plurality of image locations are obtained (e.g., via analysis of the image and/or from storage). In some instances, each of the image locations corresponds to a different one of the plurality of biological structures and indicates a location of a depiction of the biological structure within the image”; Zuiderveld, [0097] At block 1412, for each of the plurality of image locations and based on information from the first binary mask, the state of the first binary membership value of a pixel that corresponds to the image location is stored to a data structure that is associated with the image location. In some aspects, for each of the plurality of image locations, the data structure associated with the image location may include a first binary marker value that indicates a positivity state of a first biomarker at the corresponding biological structure”; Zuiderveld, [0109] “a plurality of seed locations in the image are obtained (e.g., via analysis of the image and/or from storage). In some instances, each of the image locations corresponds to a different one of the plurality of biological structures and indicates a location of a depiction of the biological structure within the image”).
However, while Zuiderveld teaches using first and second criteria for performing distance transformation, it does not explicitly recite:
obtain threshold data indicative of a plurality of thresholds for performing a distance transformation;
generate, using at least two thresholds of the plurality of thresholds, reference data by applying the distance transformation to the reference sample data;
In the same field of endeavor, Remiszewski teaches:
obtain threshold data indicative of a plurality of thresholds for performing a distance transformation (Remiszewski, [0204] “The method may also include applying a threshold value to the spectral image and the visual image 506 and generating a binary spectral image and a binary visual image based on the applied threshold value 508. The computing system may automatically select a threshold value to apply to the spectral image and a threshold value to apply to the visual image. In addition, the computing system may receive the threshold values from a user of the computing system”);
generate, using at least two thresholds of the plurality of thresholds, reference data by applying the distance transformation to the reference sample data (Remiszewski, [0206] Referring back to FIG. 5A, the computing system may receive the selected threshold values and generate a binary spectral image and a binary visual image based on the applied threshold values… The computing system may map all the pixels in the spectral image and the visual image into black and white using the threshold values for each respective image. By generating a binary image (e.g., a black and white image), the interstitial spaces between the tissues may be highlighted, as well as the basic structure of the biological sample, any morphological (e.g., shape) features in the biological sample, and/or the shape of stained regions within the tissue”).
Zuiderveld and Remiszewski are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zuiderveld with the invention of Remiszewski to make the invention that obtains plurality of thresholds for performing distance transformation and generate reference data by applying the distance transformation to the reference sample data using at least two thresholds of the plurality of thresholds; doing so the pixels with number above the threshold may be converted to white while each pixel with a number below the threshold value may be converted to black and the computing system may map all pixels in the spectral image and the visual image into black and white using the threshold values for each respective image and generate a binary image wherein the spaces between the tissues and the basic structure and/or shape of the biological sample may be highlighted (Remiszewski, [02026]); thus, one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 2, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein generating the reference data comprises applying a mapping function to a distance transformed first image of the sample (Remiszewski, [0159] “the images may be registered by point mapping to bring an image into alignment with another image. In point mapping, control points on both of the images that identify the same feature or landmark in the images are selected. Based on the positions of the control points, spatial mapping of both images may be performed”).
Reasons for combining Zuiderveld and Remiszewski are similar to that stated in the rejection of claim 1. In addition, this same reasoning is pertinent and applicable to the rejections of claims 4 – 16 below.
Regarding Claim 4, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein the distance transformation is a grayscale distance transformation (Remiszewski, [0157] “ once the spectral and visual images have been acquired, the visual image of the stained tissue may be registered with a digitally stained grayscale or pseudo-color spectral image, as indicated in 304 of the flowchart of FIG. 3. In general, image registration is the process of transforming or matching different sets of data into one coordinate system”; Remiszewski, [0189] “The distance may be a measure of the grayscale pixel-by-pixel errors summed over the whole image”).
Regarding Claim 5, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein the apparatus is configured to generate, based on the structure image data and the reference data, superposition image data indicative of the structure of the image (Zuiderveld, [0098] “for each of the plurality of image locations, the data structure associated with the image location may include a first binary marker value that indicates a positivity state of a first biomarker at the corresponding biological structure. In such case, method 1400 may further include, for each of a plurality of overlapping tiles of the image, calculating a distance transform array that includes, for each pixel of the tile, a corresponding value that indicates a distance between the pixel and a closest among the plurality of image locations for which the first binary marker value indicates a first positivity state”).
Regarding Claim 6, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein a threshold of the plurality of thresholds for performing the distance transformation is an intensity threshold for retaining or for excluding pixels for performing the distance transformation (Remiszewski, [0122] “The “best” spectrum is determined by analysis of peak position and intensity criteria, both of which vary during phase correction”; Remiszewski, [0206] “e computing system may receive the selected threshold values and generate a binary spectral image and a binary visual image based on the applied threshold values. For example, each pixel with a number above the threshold value may be converted to white, while each pixel with a number below the threshold value may be converted to black. The computing system may map all the pixels in the spectral image and the visual image into black and white using the threshold values for each respective image. By generating a binary image (e.g., a black and white image), the interstitial spaces between the tissues may be highlighted, as well as the basic structure of the biological sample, any morphological (e.g., shape) features in the biological sample, and/or the shape of stained regions within the tissue”).
Regarding Claim 7, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein the apparatus is configured to generate, based on the reference sample data and the threshold data, intermediate binary data indicative of a plurality of binary-like representations or a plurality of binary masks for performing the distance transformation and wherein generating the reference data comprises applying the distance transformation to the binary data for generating a plurality of distance transformed first images (Zuiderveld, [0096] “At block 1408, a first binary mask for the image is obtained (e.g., via analysis of the image and/or from storage). The first binary mask indicates, for each of the plurality of pixels of the image, a corresponding state of a first binary membership value… Zuiderveld, [0097] “for each of the plurality of image locations and based on information from the first binary mask, the state of the first binary membership value of a pixel that corresponds to the image location is stored to a data structure that is associated with the image location… Zuiderveld, [0099] “for each of the plurality of image locations, the data structure associated with the image location may include a second binary marker value that indicates a positivity state of a second biomarker at the corresponding biological structure, and each of the plurality of tiles may include an inner region that does not overlap the inner region of any other tile among the plurality of tiles. In such case, method 1400 may further include, for each of the inner regions, storing values of the distance transform array of the corresponding tile that correspond to image locations for which the second binary marker value indicates a first positivity state”).
Regarding Claim 8, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein the first channel and the second are part of one multiplexed image set (Zuiderveld, [0017] “the image may be a multiplexed immunofluorescence image having a plurality of channels; Zuiderveld, [0059] “FIG. 1 shows an example of a pseudocolor version of a multiplexed immunofluorescence (MPX) image in which a corresponding pseudocolor (e.g., pink, blue, green) is assigned to each of the different channels of the MPX image”; Zuiderveld, [0105] At block 1608, a plurality of locations of a first biomarker in the image are obtained. In some instances, the plurality of seed locations are obtained from a first channel of the image, and the plurality of locations of a first biomarker are obtained from a second channel of the image.).
Regarding Claim 9, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein generate the structure image data is a segmentation-free process (Zuiderveld, Fig. 16 describes the method for analyzing an image of a tissue section as a segmentation free process using distance transform array, see Zuiderveld, [0104] – [0108]).
Regarding Claim 10, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein generating the structure image data retains an unsegmented characteristic of the first image of the sample and the second image of the sample (Zuiderveld, [0108] “analysis results are provided that include a result of detecting, based on the stored indications, co-localization of at least two phenotypes in at least a portion of the tissue section…detecting co-localization of the at least two phenotypes includes detecting that a first phenotype of the at least two phenotypes occurs within a predetermined neighborhood of a second phenotype of the at least two phenotypes”).
Regarding Claim 11, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein generating the structure image data comprises emphasizing a part of the second image corresponding to a part of the reference data with a short distance and/or attenuating a part of the second image corresponding to a part of reference data with a long distance (Zuiderveld, [0086] “One example of such a computation may include the following operations: (1) For each occurrence of phenotype A in the MPX image (or selected portion thereof), find and record the distance (e.g., Euclidean distance) to the closest occurrence of phenotype B. (2) Optionally, calculate a histogram of the recorded distances (e.g., count the number of distances that are 10 microns or less, the number of distances that are greater than 10 microns and 20 microns or less, . . . , the number of distances that are greater than 90 microns and 100 microns or less, etc)”; Zuiderveld, [0090] “a distance transform array is computed for the marked tile. For each pixel of the marked tile, the distance transform array has a corresponding value that indicates the distance, within the marked tile, from that pixel to the closest marked pixel”).
Regarding Claim 12, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein generating the structure image data comprises emphasizing a spatial feature in the sample image data that corresponds to a structure represented by distance values in the reference data (Zuiderveld, [0086] “ to compute spatial relationships among different biomarkers in specific regions of interest (for example, in tumors and active stromal areas). Knowledge of such complex spatial relationships may enable a better understanding of relations among different biomarkers/phenotypes and region areas (such as blood vessels, active stroma, tumor, etc.). One example of such a computation may include the following operations: (1) For each occurrence of phenotype A in the MPX image (or selected portion thereof), find and record the distance (e.g., Euclidean distance) to the closest occurrence of phenotype B “; Zuiderveld, [0087] “To support computation of spatial relationships among different biomarkers in specific regions of interest, it may be desired to provide a method that may be used for efficiently calculating, for each occurrence of one selected phenotype, the distance to the closest occurrence of a different selected phenotype”) .
Regarding Claim 13, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein the reference sample data is raw reference sample data of the first image of the sample and the distance transformation for generating the reference data is applied to the raw reference sample data (Zuiderveld, [0027] “systems that can be used effectively obtain data from large DP images (e.g., MPX images) and quickly process statistical analysis and calculations in and among such images…. distance transform calculation over overlapping image regions supports efficient calculation of distances and distributions”; Zuiderveld, [0059] “A multiplexed immunofluorescence (MPX) image of a tissue section may be obtained by staining the section with two or more fluorophores that, upon excitation (e.g., by ultraviolet light), emit light at different respective wavelengths”; Zuiderveld, [0090] At block 1108, a distance transform array is computed for the marked tile. For each pixel of the marked tile, the distance transform array has a corresponding value that indicates the distance, within the marked tile, from that pixel to the closest marked pixel. At block 1112, the values of the distance transform array that correspond to image locations that meet the second criterion are selected and stored”).
Regarding Claim 14, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 13, wherein the sample data is raw sample data of the second image, and generating the structure image data comprises superposition of the reference data with the raw sample data (Remiszewski, [0186] “The method may additionally include generating a registered image aligning the received spectral image and the received visual image based upon the alignment of the visual image coordinates and the spectral image coordinates 420. For example, the computing system may overlay the spectral image on the visual image using the alignment of the visual image coordinates with the spectral image coordinates and automatically generate a registered image”; Remiszewski, [0189] “One optimization may include minimizing the distance between the spectral image and the visual images in the overlaid images that are mapped in the same coordinate system”).
Regarding Claim 15, Zuiderveld in view of Remiszewski teaches the apparatus according to claim 1, wherein the first image of the sample is indicative of a first biomarker and the second image of the sample is indicative of a second biomarker (Zuiderveld, [0081] “The results of primary analysis of an MPX image may include localization of multiple biomarkers by such analysis, which may be used to detect co-localization of biomarkers and phenotypes”; Zuiderveld, [0092] “FIG. 12 illustrates an application of method 1100 to computing, recording, and sorting the distance between a first biomarker and a second biomarker. In this example, the first criterion is expression of the CD8 biomarker and the second criterion is expression of the PanCK biomarker….FIG. 13 illustrates a similar application of method 1100 to computing, recording, and sorting the distance between a first biomarker and a second biomarker in which the CD8 marker occurs at multiple locations”).
Regarding Claim 16, Zuiderveld in view of Remiszewski teaches an optical imaging system, comprising the apparatus of claim 1 (Zuiderveld, [0064] “Techniques as disclosed herein may be used, for example, to design an effective readout analysis system to effectively fetch MPX data, efficiently process a whole slide image analysis, and/or perform corresponding statistical analysis”; See rejection of claim 1 above).
Regarding Claim 17, Zuiderveld teaches:
A method for an optical imaging system (Zuiderveld, [0006] “Apparatuses and methods for optimized data processing for medical image analysis are provided”), comprising:
obtaining reference sample data indicative of a first image of a sample from a first channel (Zuiderveld, [0017]” the image may be a multiplexed immunofluorescence image having a plurality of channels”; [0030] FIG. 1 shows an example of a multiplexed immunofluorescence (MPX) image; Zuiderveld, [0059] “ FIG. 1 shows an example of a pseudocolor version of a multiplexed immunofluorescence (MPX) image in which a corresponding pseudocolor (e.g., pink, blue, green) is assigned to each of the different channels of the MPX image”; [0105] “At block 1608, a plurality of locations of a first biomarker in the image are obtained. In some instances, the plurality of seed locations are obtained from a first channel of the image”);
obtaining threshold data indicative of a plurality of thresholds for performing a distance transformation (Zuiderveld, [0089] “FIG. 11B is a flowchart illustrating an example of a method 1100 for computing distances between locations of a medical image (e.g., an MPX image) that meet first and second criteria… pixels in a blank tile that correspond to image locations which meet a first criterion are marked to produce a marked tile”);
generating, using at least two thresholds of the plurality of thresholds, reference data by applying the distance transformation to the reference sample data (Zuiderveld, [0090] At block 1108, a distance transform array is computed for the marked tile. For each pixel of the marked tile, the distance transform array has a corresponding value that indicates the distance, within the marked tile, from that pixel to the closest marked pixel. At block 1112, the values of the distance transform array that correspond to image locations that meet the second criterion are selected and stored. The second criterion may be, for example, a second selected phenotype (or a second combination of selected phenotypes), possibly further limited to a particular tissue region. In such manner, an instance of method 1100 may be performed (e.g., in parallel) for each tile of interest”; Zuiderveld, [0106] “a first distance transform array for at least a portion of the image that includes the plurality of seed locations is calculated, each value of the first distance transform array corresponding to a respective pixel among the plurality of pixels and indicating a distance from the pixel to a closest among the plurality of locations of the first biomarker”);
obtaining sample data indicative of a second image of the sample from a second channel different from the first channel (Zuiderveld, [0105] “the plurality of locations of a first biomarker are obtained from a second channel of the image”); and
generating, based on the reference data and the sample data, structure image data indicative of a structure of the sample (Zuiderveld, [0095] “Referring to FIG. 14, at block 1404, a plurality of image locations are obtained (e.g., via analysis of the image and/or from storage). In some instances, each of the image locations corresponds to a different one of the plurality of biological structures and indicates a location of a depiction of the biological structure within the image”; Zuiderveld, [0097] At block 1412, for each of the plurality of image locations and based on information from the first binary mask, the state of the first binary membership value of a pixel that corresponds to the image location is stored to a data structure that is associated with the image location. In some aspects, for each of the plurality of image locations, the data structure associated with the image location may include a first binary marker value that indicates a positivity state of a first biomarker at the corresponding biological structure”; Zuiderveld, [0109] “a plurality of seed locations in the image are obtained (e.g., via analysis of the image and/or from storage). In some instances, each of the image locations corresponds to a different one of the plurality of biological structures and indicates a location of a depiction of the biological structure within the image”).
However, while Zuiderveld teaches using first and second criteria for performing distance transformation, it does not explicitly recite:
obtaining threshold data indicative of a plurality of thresholds for performing a distance transformation;
generating, using at least two thresholds of the plurality of thresholds, reference data by applying the distance transformation to the reference sample data;
In the same field of endeavor, Remiszewski teaches:
obtain threshold data indicative of a plurality of thresholds for performing a distance transformation (Remiszewski, [0204] “The method may also include applying a threshold value to the spectral image and the visual image 506 and generating a binary spectral image and a binary visual image based on the applied threshold value 508. The computing system may automatically select a threshold value to apply to the spectral image and a threshold value to apply to the visual image. In addition, the computing system may receive the threshold values from a user of the computing system”);
generate, using at least two thresholds of the plurality of thresholds, reference data by applying the distance transformation to the reference sample data (Remiszewski, [0206] Referring back to FIG. 5A, the computing system may receive the selected threshold values and generate a binary spectral image and a binary visual image based on the applied threshold values… The computing system may map all the pixels in the spectral image and the visual image into black and white using the threshold values for each respective image. By generating a binary image (e.g., a black and white image), the interstitial spaces between the tissues may be highlighted, as well as the basic structure of the biological sample, any morphological (e.g., shape) features in the biological sample, and/or the shape of stained regions within the tissue”).
Zuiderveld and Remiszewski are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zuiderveld with the invention of Remiszewski to make the invention that obtains plurality of thresholds for performing distance transformation and generate reference data by applying the distance transformation to the reference sample data using at least two thresholds of the plurality of thresholds; doing so the pixels with number above the threshold may be converted to white while each pixel with a number below the threshold value may be converted to black and the computing system may map all pixels in the spectral image and the visual image into black and white using the threshold values for each respective image and generate a binary image wherein the spaces between the tissues and the basic structure and/or shape of the biological sample may be highlighted (Remiszewski, [02026]); thus, one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 18, Zuiderveld in view of Remiszewski teaches A non-transitory computer readable medium including code, when executed, to cause a machine to perform the method of claim 17 (Zuiderveld, [0113] “respectively, may be embodied on a non-transitory computer readable medium, for example, but not limited to, a memory or other non-transitory computer readable medium known to those of skill in the art, having stored therein a program including computer executable instructions for making a processor, computer, or other programmable device execute the operations of the methods”; see rejection for claim 17 above).
Allowable Subject Matter
Claim 3 is 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 and overcoming claim objections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180240239 A1 PERFORMING SEGMENTATION OF CELLS AND NUCLEI IN MULTI-CHANNEL IMAGES Systems and methods of improving segmentation and classification in multi-channel images of biological specimens are described herein. Image channels may be arranged and processed sequentially, with the first image channel being processed from an unmodified image, and subsequent images processed from attenuated images that remove features of previous segmentations. For each segmented image channel in sequence, a binary image mask of the image channel may be created. A distance transform image may be computed from the binary mask. Attenuation images computed for all previous channels may be combined with a current attenuation image to create a combined attenuation image.
US 20240177320 A1 3D SEGMENTATION OF LESIONS IN CT IMAGES USING SELF-SUPERVISED PRETRAINING WITH AUGMENTATION A method or system for training a convolutional neural network (CNN) for medical imaging analysis. The system pre-trains the CNN's encoder using a dataset of unlabeled 3D medical images. Each 3D image includes an annotated slice delineating a boundary of a lesion and multiple non-annotated 2D slices above and below the annotated slice. The system then fine-tunes the pre-trained encoder using an annotated 2D image dataset. The annotated 2D image dataset includes multiple 2D slices of lesions, each including an annotation that delineates a boundary of a corresponding lesion.
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VAISALI RAO. KOPPOLU
Examiner
Art Unit 2664
/VAISALI RAO KOPPOLU/Examiner of Art Unit 2664
Prosecution Insights