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公开(公告)号：US11823046B2
公开(公告)日：2023-11-21
申请号：US17247786
申请日：2020-12-23
IPC分类号： G06N3/08 , G06N20/10 , G06V10/44 , G06F18/214 , G06N3/045 , G06V10/774 , G06V10/82
CPC分类号： G06N3/08 , G06F18/214 , G06N3/045 , G06N20/10 , G06V10/454 , G06V10/774 , G06V10/82 , G06V2201/03
摘要： A method and system for automatically inferring a subject's body position in a two-dimensional image produced by a medical-imaging system are disclosed. The image is labeled with a body position selected from a semantically meaningful set of candidate positions sequenced in order of their relative locations in a subject's body. A processor performs procedures that each identify a class of image features related to pixel intensity, such as a histogram of gradients, local binary patterns, or Haar-like features. A second set of procedures employs applications of a pretrained convolutional neural network that has learned to recognize features of a specific class of medical images. The results of both types of procedures are then mapped by a pretrained support-vector machine onto candidate image labels, which are mathematically combined into a single, semantically meaningful, label most likely to identify a body position of the subject shown by the image.
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2.发明授权公开(公告)号：US11687621B2
公开(公告)日：2023-06-27
申请号：US17215734
申请日：2021-03-29
IPC分类号： G06F18/25 , G06T7/00 , G06N20/00 , G06V30/194 , G06F18/214
CPC分类号： G06F18/251 , G06F18/214 , G06N20/00 , G06T7/0012 , G06V30/194 , G06T2207/10081 , G06T2207/10088 , G06T2207/10116 , G06T2207/10132 , G06T2207/20081
摘要： A joint multimodal fusion computer model architecture is provided that receives prediction output data from a machine learning (ML) computer model set comprising a plurality of different subsets of ML computer models operating on input data of different modalities and generating different prediction outputs. Prediction outputs are fused by executing an uncertainty and correlation weighted (UCW) joint multimodal fusion operation on the prediction outputs to generate a fused output providing multimodal prediction output data. The UCW joint multimodal fusion operation applies different weights to different ones of prediction outputs from the different subsets of ML computer models operating on input data of different modalities. The different weights are determined based on an estimation of uncertainty in each of the different subsets of ML computer models and an estimate of a correlation between different modalities.
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公开(公告)号：US11244755B1
公开(公告)日：2022-02-08
申请号：US17061669
申请日：2020-10-02
发明人： Tanveer Syeda-Mahmood , Chun Lok Wong , Joy Tzung-yu Wu , Yaniv Gur , Anup Pillai , Ashutosh Jadhav , Satyananda Kashyap , Mehdi Moradi , Alexandros Karargyris , Hongzhi Wang
摘要： Mechanisms are provided to implement an automated medical imaging report generator which receives an input medical image and inputs the input medical image into a machine learning (ML) computer model trained to predict finding labels based on patterns of image features extracted from the medical image. The ML computer model generates a prediction of a finding label applicable to the input medical image in terms of a finding label prediction output vector. Based on the finding label prediction output vector, a lookup operation is performed, in a medical report database of previously processed medical imaging report data structures, to find a matching medical imaging report data structure corresponding to the finding label. An output medical imaging report is generated for the input medical image based on natural language content of the matching medical imaging report data structure.
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公开(公告)号：US20210303931A1
公开(公告)日：2021-09-30
申请号：US16836115
申请日：2020-03-31
摘要： Methods and systems for training a model labeling two or more organic structures in an image. One method includes receiving a set of training images including a first plurality of images and a second plurality of images. Each of the first plurality of images including a label for a first subset of the two or more organic structures and each of the second plurality of images including a label for a second subset of the two or more organic structures, the second subset being different than the first subset. The method also includes training the model using the first plurality of images, the second plurality of images, and a label merging function mapping a label included in the first plurality of images to a label included in the second plurality of images.
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公开(公告)号：US10592820B2
公开(公告)日：2020-03-17
申请号：US15178511
申请日：2016-06-09
发明人： Yu Cao , Tanveer Syeda-Mahmood , Hongzhi Wang
摘要： Sequential learning techniques, such as auto-context, that apply the output of an intermediate classifier as contextual features for its subsequent classifier have shown impressive performance for semantic segmentation. It is shown that these methods can be interpreted as an approximation technique derived from a Bayesian formulation. To improve the effectiveness of applying this approximation technique, a new sequential learning approach is proposed for semantic segmentation that solves a segmentation problem by breaking it into a series of simplified segmentation problems. Sequentially solving each of the simplified problems along the path leads to a more effective way for solving the original segmentation problem. To achieve this goal, a learning-based method is proposed to generate simplified segmentation problems by explicitly controlling the complexities of the modeling classifiers. Promising results were reported on the 2013 SATA canine leg muscle segmentation dataset.
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公开(公告)号：US20200020107A1
公开(公告)日：2020-01-16
申请号：US16255140
申请日：2019-01-23
摘要： A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions executed by the processor to specifically configure the processor to implement a multi-atlas segmentation engine. An offline registration component performs registration of a plurality of atlases with a set of image templates to thereby generate and store, in a first registration storage device, a plurality of offline registrations. The atlases are annotated training medical images and the image templates are non-annotated medical images. The multi-atlas segmentation engine receives a target image. An image selection component selects a subset of image templates in the set of image templates based on the target image. An online registration component performs registration of the subset of image templates with the target image to generate a plurality of online registrations. The multi-atlas segmentation engine retrieves offline registrations corresponding to the subset of image templates from the first registration storage device. The multi-atlas segmentation engine performs segmentation of the target image based on the retrieved offline registrations corresponding to the subset of image templates and the plurality of online registrations. The segmentation applies labels to anatomical structures present in the target image based on the retrieved offline registrations and the plurality of online registrations to thereby output a modified target image.
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公开(公告)号：US10467767B2
公开(公告)日：2019-11-05
申请号：US15389860
申请日：2016-12-23
摘要： Registration-based interpolation between images slices is provided. In various embodiments, a plurality of 2D images is read. Each of the plurality of 2D images represents a slice of a 3D volume. A plurality of annotations is read for a subset of the plurality of 2D images. The annotations comprise at least one anatomical label. 2D images lacking annotations are selected from the plurality of 2D images. The at least one anatomical label is propagated from the subset of the plurality of 2D images to the selected 2D images by deformable registration.
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8.发明授权公开(公告)号：US10410384B2
公开(公告)日：2019-09-10
申请号：US16363330
申请日：2019-03-25
发明人： Hongzhi Wang
摘要： Computationally efficient anatomy segmentation through low-resolution multi-atlas label fusion and corrective learning is provided. In some embodiments, an input image is read. The input image has a first resolution. The input image is downsampled to a second resolution lower than the first resolution. The downsampled image is segmented into a plurality of labeled anatomical segments. Error correction is applied to the segmented image to generate an output image. The output image has the first resolution.
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公开(公告)号：US10395335B2
公开(公告)日：2019-08-27
申请号：US15470721
申请日：2017-03-27
发明人： Hongzhi Wang , Rui Zhang
摘要： One embodiment provides a method comprising receiving image data with a first image resolution, and determining an optimal image resolution for sampling the image data based on a learned model. The optimal image resolution is lower than the first image resolution. The method further comprises sampling the image data at the optimal image resolution, and performing image analysis on sampled image data resulting from the sampling.
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10.发明申请公开(公告)号：US20180061091A1
公开(公告)日：2018-03-01
申请号：US15253326
申请日：2016-08-31
发明人： Hongzhi Wang
CPC分类号： G06T11/008 , A61B6/032 , G06K9/6202 , G06K9/66 , G06T7/11 , G06T7/30 , G06T2207/10081 , G06T2207/20081 , G06T2207/30048
摘要： Computationally efficient anatomy segmentation through low-resolution multi-atlas label fusion and corrective learning is provided. In some embodiments, an input image is read. The input image has a first resolution. The input image is downsampled to a second resolution lower than the first resolution. The downsampled image is segmented into a plurality of labeled anatomical segments. Error correction is applied to the segmented image to generate an output image. The output image has the first resolution.
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