公开(公告)号：US20250069231A1

公开(公告)日：2025-02-27

申请号：US18814456

申请日：2024-08-23

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Xiao XUE ,  Gengwan LI ,  Bing HAN ,  Bing LI

IPC: G06T7/11

Abstract: An image segmentation apparatus according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to obtain a massive region to which labeling information is attached and a tubular region to which labeling information is attached. By using the labeling information of the massive region and the labeling information of the tubular region, the processing circuitry is configured to generate a region to be segmented and a non-boundary region, by carrying out a distance transformation. The processing circuitry is configured to generate a classifier for classifying spatial coordinates, by using the labeling information of the non-boundary region and labeling information in a specific position determined on the basis of the region to be segmented and the tubular region. The processing circuitry is configured to segment voxels in the region to be segmented by using the classifier and to thus determine a final segmentation result.

公开(公告)号：US20230394791A1

公开(公告)日：2023-12-07

申请号：US18329832

申请日：2023-06-06

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Sha WANG ,  Bing HAN ,  Fanjie MENG ,  Qiqi XU ,  Ye YUE

IPC: G06V10/764 ,  G06T7/11 ,  G06V10/25

CPC classification number: G06V10/764 ,  G06T7/11 ,  G06V10/25

Abstract: An image processing method according to an embodiment includes a specifying step, an inference step, and an integration step. In the specifying step, a first portion including a region corresponding to an anatomical site of a target and a second portion including a region different from the anatomical site are specified in the image. In the inference step, by using a deep learning model, segmentation of the region corresponding to the anatomical site is performed on the first portion and segmentation of the region different from the anatomical site is performed on the second portion, or classification and detection of an image including the region corresponding to the anatomical site is performed on the first portion and classification and detection of an image including the region different from the anatomical site is performed on the second portion. In the integration step, results of the respective processes are integrated for output.

公开(公告)号：US20230019622A1

公开(公告)日：2023-01-19

申请号：US17811607

申请日：2022-07-11

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Fanjie MENG ,  Bing HAN ,  Sha WANG ,  Ye YUE ,  Xu YANG ,  Tianhong LI

IPC: G06N20/00

Abstract: A model training device according to an embodiment of the present disclosure includes processing circuitry. The processing circuitry is configured to obtain an initial learning model by learning a data set including medical images as learning data. The processing circuitry is configured to evaluate the initial learning model by using a global metric, so as to obtain error data sets each having an outlier from among a plurality of data sets used in the evaluation. The processing circuitry is configured to obtain a plurality of error data set groups by grouping the plurality of error data sets while using a local metric. The processing circuitry is configured to specify model training information with respect to each of the error data set groups.

公开(公告)号：US20240347175A1

公开(公告)日：2024-10-17

申请号：US18633744

申请日：2024-04-12

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Fuyue WANG ,  Bing HAN ,  Qiqi XU

IPC: G16H30/40 ,  G06V10/26 ,  G06V10/72 ,  G06V10/774 ,  G06V10/82

CPC classification number: G16H30/40 ,  G06V10/26 ,  G06V10/72 ,  G06V10/7753 ,  G06V10/82

Abstract: A medical image processing method according to an embodiment of the present disclosure includes: training a deep neural network by using labeled image data; obtaining a first augmented image by carrying out a weak data augmentation on unlabeled image data; performing a predicting process on the first augmented image by using the deep neural network and determining whether each of the pixels in the first augmented image is able to serve as a pseudo-label on the basis of prediction information of the pixel; obtaining a second augmented image by carrying out a strong data augmentation on the first augmented image; training the deep neural network by using the second augmented image and the pseudo-labels; and updating the deep neural network on the basis of training results of the labeled image data and the unlabeled image data and processing a medical image by using the updated deep neural network.

公开(公告)号：US20240303821A1

公开(公告)日：2024-09-12

申请号：US18599801

申请日：2024-03-08

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Xiao XUE ,  Gengwan LI ,  Bing HAN

IPC: G06T7/11 ,  G06T7/00 ,  G16H30/20 ,  G16H30/40

CPC classification number: G06T7/11 ,  G06T7/0012 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/20081 ,  G06T2207/30056 ,  G06T2207/30061 ,  G06T2207/30101

Abstract: A segmentation model learning method according to an embodiment includes learning that, based on a loss function value, includes performing supervised learning of the voxels in medical image data according to the region to which the voxels belong. The learning of the medical image data includes: using first-type labeling information, which is meant for segmenting a predetermined structure into a plurality of categories, about the voxels of a predetermined structure and causing a segmentation model to perform direct supervised learning that represents learning for segmentation of the predetermined structure into a plurality of categories; using second-type labeling information, which is meant for segmenting a massive region covering the predetermined structure into a plurality of blocks, about the voxels of a massive region and causing the segmentation model to perform indirect supervised learning that represents learning for segmentation of the massive region into a plurality of categories; and optimizing the network parameters of the segmentation model.