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.

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：US20230245331A1

公开(公告)日：2023-08-03

申请号：US18160364

申请日：2023-01-27

Applicant: CANON MEDICAL SYSTEMS CORPORATION

Inventor： Panjie GOU ,  Xiao XUE ,  Shun ZHAO ,  Qilin XIAO

IPC: G06T7/33 ,  G06T7/13 ,  A61B8/00 ,  A61B8/08

CPC classification number: G06T7/337 ,  G06T7/13 ,  A61B8/469 ,  A61B8/5223 ,  A61B8/463 ,  A61B8/5238 ,  G06T2207/10132 ,  G06T2207/20221 ,  G06T2207/30081 ,  G06T2207/10088 ,  G06T2207/10081

Abstract: A medical image processing apparatus according to the present embodiment includes processing circuitry. The processing circuitry extracts a registration target area corresponding to a registration target organ from an ultrasonic image by acquiring edges of the registration target organ from the ultrasonic image among a plurality of types of medical images including the registration target organ. The processing circuitry calculates the degree of deformation of a plurality of positions in the registration target area. The processing circuitry performs a rigid registration on the types of medical images on the basis of the degree of deformation for each position calculated at the positions and the similarity between the types of medical images at the positions.