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 .
Response to Amendment
Claims 1 and 15 have been amended.
Claims 1-21 are still pending for consideration.
Drawings
The drawing objection is withdrawn in view of Applicant’s persuasive arguments presented in the response filled on Feb 19, 2016. No corrected drawings are required.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 4, 6-9, 12-15, 18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Olender et al. (US 20230076868 A1) in view of Lau et al. (US 20210249142 A1).
Regarding claim 1, Olender et al. teach a method for generating synthetic medical images representing an anatomy of interest and an anomaly within said anatomy of interest (see Abstract; “a medical image having at least one obscured region includes an input for receiving a first classification map generated …The pre-processing module also generates a second classification map that has the at least one obscured region filled in. The system also includes a generative network coupled to the pre-processing module and configured to generate a synthetic OCT image based on the second classification map”), training a neural network to generate a synthetic medical image from a segmentation mask (see para [0029]; “The neural network may be used to generate synthetic medical images of tissues or systems which may or may not have a basis in a physical, existing tissue or system……a generative network may be trained to generate an optical coherence tomography (OCT) image based on a classification map”, see also para [0005; “a generative network coupled to the pre-processing module and configured to generate a synthetic OCT image based on the second classification map” Note; segmentation mask correspond to classification map); the generation of an artificial segmentation mask comprising combining a segmentation of the anatomy of interest of a majority segmentation mask with a segmentation of the anomaly of a minority segmentation mask (see para [0010]; “configured to create a merged classification map based on the first classification map and the second classification map. … The generative network is configured to generate a synthetic merged image based on the merged classification map”, see also para [0005]; “a first classification map generated using an acquired optical coherence tomography (OCT) image having at least one obscured region…..a second classification map that has the at least one obscured region filled in”); and generating synthetic medical images from the artificial segmentation masks using the previously trained neural network (see para [0056]; “The merged classification map 1208 is provided as an input to a trained generative network 1210 such as, for example, generative network 104 shown in FIG. 1. The trained generative network 1210 (including generator 1212) generates a synthetic merged image 1214 based on the merged classification map 1208”); generating artificial segmentation masks from majority segmentation masks and from minority segmentation masks (see para [0010]; “configured to create a merged classification map based on the first classification map and the second classification map. The merged classification map includes information from at least one of the first classification map and the second classification map. The system further includes a generative network coupled to the pre-processing module. The generative network is configured to generate a synthetic merged image based on the merged classification map”). However, Olender does not teach said method comprising: generating majority segmentation masks, each majority segmentation mask being associated with a majority real medical image representing the anatomy of interest of a patient without an anomaly; generating minority segmentation masks, each minority segmentation mask being associated with a minority real medical image representing the anatomy of interest of a patient with an anomaly; selecting artificial segmentation masks based on the location of the anomaly within the organ of interest or in relation to other organs or anatomical structures.
In the same field of endeavor Lau et al. teaches said method comprising: generating majority segmentation masks, each majority segmentation mask being associated with a majority real medical image representing the anatomy of interest of a patient without an anomaly (see para [0005]; “a plurality of batches of labeled image sets and corresponding segmentation masks…. simulates abnormalities on at least some of the images and their corresponding segmentation masks without abnormalities”, see also para [0007]; “segmentation masks with real abnormalities and segmentation masks with simulated abnormalities from a mask generator…. the learning data may include images without abnormalities and images with simulated abnormalities from a refining generator”); generating minority segmentation masks, each minority segmentation mask being associated with a minority real medical image representing the anatomy of interest of a patient with an anomaly (see para [0016]; “The segmentation network may receive learning data including a plurality of batches of training images and training segmentation masks with and without abnormalities”, see also para [0087]; “segmentation networks can be trained with images with simulated abnormalities in conjunction with images with and without real abnormalities”, and para [0052]; “segmentation mask with real abnormalities (103) or a mask with simulated abnormalities” Note; minority segmentation masks with an anomaly correspond to segmentation masks with real abnormalities), selecting artificial segmentation masks based on the location of the anomaly within the organ of interest or in relation to other organs or anatomical structures (see para [0040]; “a mask generator to simulate the shape of the abnormalities given an input segmentation mask of certain anatomical structures”, see also para [0082]; “a final post-processing step for the simulated image includes… removes any artifacts created by the refining generator outside the certain anatomical structures where abnormalities are unlikely to be located….scars can be confined in the left myocardium as scars do not locate outside the myocardium”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. in order to address the issue of data imbalance with rare pathologies (see para [0005).
Regarding claim 4, the rejection of claim 1 is incorporated herein.
Olender et al. in the combination further teach wherein the generation of an artificial segmentation mask further comprises checking that the segmentation of the anomaly relative to the segmentation of the anatomy of interest meets a particular criterion (see para [0056]; “the pre-processing module 1206 may consider the reliability of all classification maps at each spatial location, and in each region take the form of the classification map considered to be most reliable in that location”).
Regarding claim 6, the rejection of claim 1 is incorporated herein.
Olender et al. in the combination further teach wherein the neural network used to generate a synthetic medical image is a generator neural network, and the training of the generator neural network is implemented using a discriminator neural network, with the generator neural network and the discriminator neural network forming a pair of generative adversarial networks (see para [0033]; “the generative network 104 is trained using a conditional generative adversarial network (cGAN). FIG. 2 is a block diagram of a conditional generative adversarial network for training a generative network and a discriminative network in accordance with an embodiment. cGAN 200 includes a generative network (or generator) 210 and a discriminative network (or discriminator) 212”).
Regarding claim 7, the rejection of claim 1 is incorporated herein.
Olender et al. in the combination further teach wherein the real medical images from which the majority segmentation masks and the minority segmentation masks are generated are medical images obtained by tomodensitometry, by positron emission tomography, by magnetic resonance imaging or by ultrasound (see para [0029]; “systems and methods described herein may be used to generate other types of medical images, e.g., IVUS, CT, MR, X-ray, ultrasound, etc”).
Regarding claim 8, the rejection of claim 1 is incorporated herein.
Lau et al. in the combination further teach wherein the anatomy of interest is a liver, a lung, a kidney, a bone or a blood vessel (see para [0014]; “The image data may be representative of a chest, including lungs and heart, or of an abdomen, including a liver”).
Regarding claim 10, the rejection of claim 1 is incorporated herein.
Lau et al. in the combination further teach a method for training a machine learning algorithm aiming to detect or to characterize an anomaly in the anatomy of interest of a patient on a real medical image, said method comprising: generating synthetic medical images representing the anatomy of interest and an anomaly within said anatomy of interest; and training the machine learning algorithm using a set of training images comprising the synthetic medical images thus generated (see para [0017]; “The training images may include real images with and without abnormalities. The training images may include images with simulated abnormalities. Images with simulated abnormalities may be generated by a refining generator”, see also para [0012]; “receive learning data including a plurality of batches of real images, segmentation masks with simulated abnormalities from the mask generator, and produces simulated abnormalities on the image using the received learning data”).
Regarding claim 12, the rejection of claim 10 is incorporated herein.
Lau et al. in the combination further teach wherein the machine learning algorithm is an anomaly classification algorithm (see para [0023]; “classify abnormalities using the classification network”).
Regarding claim 13, the rejection of claim 10 is incorporated herein.
Lau et al. in the combination further teach wherein the machine learning algorithm is an anomaly segmentation algorithm (see para [0050]; “Inputs of the mask generator include a segmentation mask of the abnormalities (101). Outputs of the mask generator include a segmentation mask that includes simulated abnormalities and segmentation mask of the abnormalities”).
Regarding claim 14, the rejection of claim 10 is incorporated herein.
Lau et al. in the combination further teach wherein the machine learning algorithm is implemented by a deep neural network (see para [0087]; “Deep convolutional networks including classification and segmentation networks can be trained with images with simulated abnormalities in conjunction with images with and without real abnormalities”).
Regarding claim 15, the scope of claim 15 is fully incorporated in claim 1, and the
rejection of claim 1 is equally applicable here
Regarding claim 18, the rejection of claim 15 is incorporated herein.
Olender et al. in the combination further teach wherein, in order to generate an artificial segmentation mask, the one or more processor(s) is/are also configured to check that the segmentation of the anomaly relative to the segmentation of the anatomy of interest meets a particular criterion (see para [0056]; “the pre-processing module 1206 may consider the reliability of all classification maps at each spatial location, and in each region take the form of the classification map considered to be most reliable in that location”).
Regarding claim 20, the rejection of claim 15 is incorporated herein.
Olender et al. in the combination further teach wherein the storage medium also stores a neural network previously trained to generate a synthetic medical image from a segmentation mask and, when the program is executed, the one or more processor(s) is/are configured to generate synthetic medical images with the neural network from artificial segmentation masks (see para [0030]; “System 100 includes a trained generative network 104 that includes a generator 106. A classification map 102 may be input to the generative network 104…. The classification map 102 may be input to the trained generator 106 which then generates a synthetic image 108 (e.g., an OCT image) based on the classification map 102”, see also para [0056]; “The merged classification map 1208 is provided as an input to a trained generative network 1210 such as, for example, generative network 104 shown in FIG. 1. The trained generative network 1210 (including generator 1212) generates a synthetic merged image 1214 based on the merged classification map 1208”).
Regarding claim 21, the rejection of claim 20 is incorporated herein.
Olender et al. in the combination further teach wherein the neural network for generating a synthetic medical image is a generator neural network adapted to be trained using a discriminator neural network, the generator neural network and the discriminator neural network forming a pair of generative adversarial networks (see para [0033]; “the generative network 104 is trained using a conditional generative adversarial network (cGAN). FIG. 2 is a block diagram of a conditional generative adversarial network for training a generative network and a discriminative network in accordance with an embodiment. cGAN 200 includes a generative network (or generator) 210 and a discriminative network (or discriminator) 212”).
Claims 2-3, 9, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Olender et al. in view of Lau et al. as applied in claims 1, and 15 above, and further in view of Shin et al. “Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks”.
Regarding claim 2, the rejection of claim 1 is incorporated herein. The combination of Olender et al. and Lau et al. as a whole does not teach wherein the generation of an artificial segmentation mask further comprises transforming the segmentation of the anomaly of the minority segmentation mask.
In the same field of endeavor, Shin et al. teach wherein the generation of an artificial segmentation mask further comprises transforming the segmentation of the anomaly of the minority segmentation mask (see page 2, 2nd para; “we can alter the tumor’s size, change its location, or place a tumor in an otherwise healthy brain”, see also page 5, Fig. 2; “possibly with alterations (shift tumor location; enlarge; shrink”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. and the use of medical image synthesis for data augmentation and anonymization using generative adversarial networks of Shin et al. in order to produce highly correlated image training data (see page 2, 2nd para).
Regarding claim 3, the rejection of claim 2 is incorporated herein.
Shin et al. in the combination further teach wherein the transformation of the segmentation of the anomaly corresponds to a rotation, a magnification, a reduction, a deformation and/or a movement of the segmentation of the anomaly (see page 5, 1st para; “We
introduce variability by adjusting those labels (e.g., changing tumor size, moving the
tumor’s location, or placing tumor on a otherwise tumor-free brain label)” see also page 6, 5th para; “The augmentation using synthetic images can be used in addition to the usual data augmentation methods such as random cropping, rotation, translation, or elastic deformation”).
Regarding claim 9, the rejection of claim 1 is incorporated herein.
Shin et al. in the combination further teach wherein the anomaly is a tumour or an ablation zone (see Abstract; “we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. and the use of medical image synthesis for data augmentation and anonymization using generative adversarial networks of Shin et al. in order to offer a potential solution of small incidence of pathological findings, and the restrictions around sharing of patient data (see Abstract).
Regarding claim 16, the rejection of claim 15 is incorporated herein.
Shin et al. in the combination further teach wherein, in order to generate an artificial segmentation mask, the one or more processor(s) is/are also configured to transform the segmentation of the anomaly of the minority segmentation mask (see page 2, 2nd para; “we can alter the tumor’s size, change its location, or place a tumor in an otherwise healthy brain”, see also page 5, Fig. 2; “possibly with alterations (shift tumor location; enlarge; shrink”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. and the use of medical image synthesis for data augmentation and anonymization using generative adversarial networks of Shin et al. in order to produce highly correlated image training data (see page 2, 2nd para).
Regarding claim 17, the rejection of claim 15 is incorporated herein.
Shin et al. in the combination further teach wherein the transformation of the segmentation of the anomaly corresponds to a rotation, a magnification, a reduction, a deformation or a movement of the segmentation of the anomaly (see page 5, 1st para; “We introduce variability by adjusting those labels (e.g., changing tumor size, moving the tumor’s location, or placing tumor on a otherwise tumor-free brain label)” see also page 6, 5th para; “The augmentation using synthetic images can be used in addition to the usual data augmentation methods such as random cropping, rotation, translation, or elastic deformation”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. and the use of medical image synthesis for data augmentation and anonymization using generative adversarial networks of Shin et al. in order to perturb the given image than the usual data augmentation techniques (see page 2, 2nd para).
Claims 5, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Olender et al. in view of Lau et al. as applied in claims above, and further in view of Olsson et al. NPL “ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning”.
Regarding claim 5, the rejection of claim 1 is incorporated herein.
Olender et al. in the combination further teach wherein a segmentation mask comprises a set of voxels, with each voxel corresponding to a zone of the real medical image with which the segmentation mask is associated, with each voxel being associated with a numerical value encoding what is shown by said zone on the real medical image (see para [0032]; “the maps may be formatted in standard data structures such as a two-dimensional (2D) class numeric label map, three-dimensional (3D) color-coded images, or multi-dimensional one-hot encodings. The associated images may be 2D greyscale or intensity images or 3D images (e.g., 3D color images)”). However, the combination Olender et al. and Lau et al. does not teach and the step of generating an artificial segmentation mask comprises: selecting a majority segmentation mask and a minority segmentation mask; identifying, on the selected minority segmentation mask, a set of voxels, the numerical value of which encodes the anomaly; and replacing, on the selected majority segmentation mask, the numerical value of the voxels identified by the numerical value encoding the anomaly.
In the same field of endeavor Olsson et al. teach and the step of generating an artificial segmentation mask comprises: selecting a majority segmentation mask and a minority segmentation mask; identifying, on the selected minority segmentation mask, a set of voxels, the numerical value of which encodes the anomaly (see Fig. 1 and text, page 1, right col. 2nd para; “We propose a segmentation-based data augmentation strategy, ClassMix, and describe how it can be used for semi-supervised semantic segmentation. The augmentation strategy cuts half of the predicted classes from one image and pastes them onto another image, forming a new sample”); and replacing, on the selected majority segmentation mask, the numerical value of the voxels identified by the numerical value encoding the anomaly (see page 1, right col. 2nd para; “The augmentation strategy cuts half of the predicted classes from one image and pastes them onto another image, forming a new sample…”, see also page 1369, 2.3; “In the CutMix algorithm [38], randomized rectangular regions are cut out from one image and pasted onto another. This technique is based on mask-based mixing, where two images are mixed using a binary mask of the same size as the images. Our proposed technique, ClassMix, is based on a similar principle of combining images and makes use of predicted segmentations to generate the binary masks, instead of rectangle”, Note: values from the minority region replace those on majority image/mask). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. and a credible and segmentation-based data augmentation for semi-supervised learning of Olsson et al. in order to solve scarcity and data imbalance by ensuring the correct edge information around the lesion (see page 1, right col. 2nd para).
Regarding claim 19, the rejection of claim 15 is incorporated herein.
Olender et al. in the combination further teach wherein a segmentation mask comprises a set of voxels, with each voxel corresponding to a zone of the real medical image with which the segmentation mask is associated, with each voxel being associated with a numerical value encoding what is shown by said zone on the real medical image (see para [0032]; “the maps may be formatted in standard data structures such as a two-dimensional (2D) class numeric label map, three-dimensional (3D) color-coded images, or multi-dimensional one-hot encodings. The associated images may be 2D greyscale or intensity images or 3D images (e.g., 3D color images)”).
Olsson et al. in the combination further teach and, in order to generate an artificial segmentation mask, the one or more processor(s) is/are configured to: select a majority segmentation mask and a minority segmentation mask; identify, on the selected minority segmentation mask, a set of voxels, the numerical value of which encodes the anomaly (see Fig. 1 and text, page 1, right col. 2nd para; “We propose a segmentation-based data augmentation strategy, ClassMix, and describe how it can be used for semi-supervised semantic segmentation. The augmentation strategy cuts half of the predicted classes from one image and pastes them onto another image, forming a new sample”); and replace, on the selected majority segmentation mask, the numerical value of the voxels identified by the numerical value encoding the anomaly(see page 1, right col. 2nd para; “The augmentation strategy cuts half of the predicted classes from one image and pastes them onto another image, forming a new sample…”, see also page 1369, 2.3; “In the CutMix algorithm [38], randomized rectangular regions are cut out from one image and pasted onto another. This technique is based on mask-based mixing, where two images are mixed using a binary mask of the same size as the images. Our proposed technique, ClassMix, is based on a similar principle of combining images and makes use of predicted segmentations to generate the binary masks, instead of rectangle”, Note: values from the minority region replace those on majority image/mask). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. and a credible and effective data augmentation for semantic segmentation of medical lesions of Olsson et al. in order to solve scarcity and data imbalance by ensuring the correct edge information around the lesion (see page 1, right col. 2nd para).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Olender et al. Lau et al. as applied in claims above, in view of Madani et al. (US 20190197368 A1), and further in view of Bashir et al. “Machine learning guided aptamer refinement and
discovery”.
Regarding claim 11, the rejection of claim 10 is incorporated herein.
The combination of Olender et al. and Lau et al. as a whole does not teach wherein the set of training images comprises synthetic medical images and real medical images and the number of images with an anomaly is at least equal to 10 % of the number of images without an anomaly.
In the same field of endeavor Madani et al. teach wherein the set of training images comprises synthetic medical images and real medical images (see para [0005]; “The method further comprises generating, by a generator of the GAN, one or more generated medical images and inputting, to the discriminator of the GAN, a training medical image set comprising a first subset of labeled medical images, a second subset of unlabeled medical images, and a third subset comprising the one or more generated medical images”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method for utilizing synthetic medical images generated using a neural network of Olender et al. in view of methods for providing a novel framework to simulate the appearance of pathology on patients of Lau et al. and adapting a generative adversarial network to new data sources for image classification of Madani et al. in order to differentiate synthetic images from true images (see para [0005]). However, the combination of Olender et al., Lau et al. and Madani et al. as a whole does not teach and the number of images with an anomaly is at least equal to 10 % of the number of images without an anomaly.
In the same field of endeavor Bashir et al. teaches and the number of images with an anomaly is at least equal to 10 % of the number of images without an anomaly (see page 8, right col. 5th para; “We upsampled positive sequences (those with a summed count of at least 1000 across all rounds) so they made at least 10% of each training batch”). Accordingly, it would have been obvious to one of ordinary skill in the art before the invention of the claimed invention to modify a method of Olender et al. and Lau et al. in view of the use of adapting a generative adversarial network to new data sources for image classification of Madani et al. and further in view of machine learning guided aptamer refinement and discovery of Bashir et al. in order to solve class imbalance problem (see page 8, right col. 5th para).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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