Granted Patent B2

US 12,482,107 · App. 18/147,688 · Granted Nov 25, 2025

Inventors: Xiang Chen (Shanghai, CN); Yang Li (Shanghai, CN); Saisai Su (Shanghai, CN)

Assignee: SHANGHAI UNITED IMAGING HEALTHCARE CO., LTD.

G06T7/11G06T7/0012G06T7/155

Abstract

Claims (83)

1 . A method for feature information determination, which is implemented on a computing device including at least one processor and at least one storage device, the method comprising:

obtaining at least one image including a subject, the subject including liver tissue, the at least one image including a first image;

determining a preliminary segmentation result including a preliminary region representative of the liver tissue by inputting the first image into a first segmentation model;

determining a target segmentation result by inputting the preliminary segmentation result into a second segmentation model, the target segmentation result including an effective liver region of the liver tissue, the effective liver region being a region of the liver tissue that excludes a liver lesion region;

determining a target image based on the target segmentation result;

determining at least one target region in the target image; and

analyzing the liver tissue of the subject by determining feature information of the at least one target region based on at least one parameter of the at least one target region.

2 . The method of claim 1 , wherein the at least one image includes a morphological image, and the determining feature information of the at least one target region based on at least one parameter of the at least one target region comprises:

obtaining a functional image corresponding to the morphological image;

determining at least one second target region in the functional image corresponding to the at least one target region in the morphological image by registering the functional image and the morphological image;

designating at least one parameter of the at least one second target region in the functional image as the at least one parameter of the at least one target region in the morphological image; and

determining the feature information of the at least one target region based on the at least one parameter of the at least one target region.

3 . The method of claim 2 , wherein

the morphological image includes at least one of a magnetic resonance imaging (MRI) image, or a computed tomography (CT) image; and

the functional image includes at least one of a diffusion functional image, a perfusion functional image, or a fat functional image.

4 . The method of claim 1 , wherein the at least one image includes a functional image, and the determining feature information of the at least one target region based on at least one parameter of the at least one target region comprises:

obtaining the at least one parameter of the at least one target region in the functional image; and

determining the feature information of the at least one target region based on the at least one parameter of the at least one target region.

5 . The method of claim 1 , further comprising:

outputting a report based on the feature information of the at least one target region.

6 . The method of claim 1 , wherein the at least one image further includes a second image, and the determining a target segmentation result by inputting the preliminary segmentation result into a second segmentation model comprises:

processing the second image based on the preliminary segmentation result to generate a processed second image; and

determining the target segmentation result by segmenting the processed second image using the second segmentation model.

7 . The method of claim 1 , further comprising:

identifying a falciform ligament of the liver tissue based on the at least one image using a falciform ligament recognition model.

8 . The method of claim 7 , wherein

the target segmentation result is a 3D image including a plurality of slices of the subject,

the determining a target image based on the target segmentation result comprises:

determining a left liver region and a right liver region in the target segmentation result based on the falciform ligament of the liver tissue; and

selecting a slice from the plurality of slices of the target segmentation result as the target image based on feature information of the plurality of slices; and

the determining the at least one target region in the target image comprises:

determining a first count of regions of interest (ROIs) in the left liver region in the target image and a second count of ROIs in the right liver region in the target image based on an area ratio of the left liver region and the right liver region in the target image; and

determining the at least one target region based on the first count of ROIs in the left liver region in the target image and the second count of ROIs in the right liver region in the target image.

9 . The method of claim 1 , wherein the determining a target image based on the target segmentation result comprises:

identifying a vascular region in the target segmentation result using a vascular recognition model; and

determining the target image by removing the vascular region from the target segmentation result.

10 . The method of claim 1 , wherein the determining the at least one target region in the target image comprises:

dividing the target image into a plurality of sub-regions;

determining at least one ROI in each of the plurality of sub-regions based on a count of ROIs and a size of an ROI, wherein the count of ROIs and the size of the ROI are set manually or determined in advance; and

determining the at least one target region based on a plurality of ROIs in the plurality of sub-regions.

11 . The method of claim 10 , wherein the determining at least one ROI in each of the plurality of sub-regions based on a count of ROIs and a size of an ROI comprises:

for each sub-region of the plurality of sub-regions,

determining a plurality of candidate ROIs in the sub-region;

selecting at least one candidate ROI from the plurality of candidate ROIs in the sub-region based on the count of ROIs and the size of the ROI as the at least one ROI in the sub-region.

12 . The method of claim 1 , wherein the determining a target image based on the target segmentation result comprises:

determining a plurality of liver segment regions of the liver tissue by inputting the target segmentation result into a liver segment recognition model; and

selecting at least one liver segment region from the plurality of liver segment regions of the liver tissue as the target image.

13 . The method of claim 1 , wherein the target segmentation result is a 3D image including a plurality of slices of the subject, and the target image is at least one slice selected from the plurality of slices.

14 . The method of claim 13 , wherein the at least one slice is selected from the plurality of slices by at least one:

selecting a slice with a largest size from the plurality of slices;

selecting a slice with a largest liver area from the plurality of slices;

selecting a slice in a middle location of the plurality of slices; or

selecting a slice located at a golden section position of the liver tissue from the plurality of slices.

15 . The method of claim 13 , wherein

the liver tissue includes a left liver region and a right liver region, and

the determining the at least one target region in the target image comprises:

determining a first count of regions of interest (ROIs) in the left liver region in the target image and a second count of ROIs in the right liver region in the target image based on an area ratio of the left liver region and the right liver region in the target image; and

determining the at least one target region based on the first count of ROIs in the left liver region in the target image and the second count of ROIs in the right liver region in the target image.

16 . The method of claim 1 , wherein the first segmentation model and the second segmentation model are trained jointly using a plurality of groups of training samples, each group of training samples including a sample image of a sample liver tissue and a reference target segmentation result including a sample effective liver region.

17 . A system for feature information determination, comprising:

at least one storage medium including a set of instructions; and

at least one processor in communication with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to cause the system to perform operations comprising:

obtaining at least one image including a subject, the subject including liver tissue, the at least one image including a first image;

determining a preliminary segmentation result including a preliminary region representative of the liver tissue by inputting the first image into a first segmentation model;

determining a target segmentation result by inputting the preliminary segmentation result into a second segmentation model, wherein the target segmentation result includes an effective liver region of the liver tissue, the effective liver region is a region of the liver tissue that excludes a liver lesion region, the second segmentation model is generated by training a preliminary segmentation model with a plurality of groups of training samples, each group of training samples includes a sample preliminary segmentation result of a sample liver tissue and a reference target segmentation result including a sample effective liver region;

determining a target image based on the target segmentation result;

determining at least one target region in the target image; and

analyzing the liver tissue of the subject by determining feature information of the at least one target region based on at least one parameter of the at least one target region.

18 . The system of claim 17 , wherein the at least one image includes a morphological image, and the determining feature information of the at least one target region based on at least one parameter of the at least one target region comprises:

obtaining a functional image corresponding to the morphological image;

determining at least one second target region in the functional image corresponding to the at least one target region in the morphological image by registering the functional image and the morphological image;

designating at least one parameter of the at least one second target region in the functional image as the at least one parameter of the at least one target region in the morphological image; and

determining the feature information of the at least one target region based on the at least one parameter of the at least one target region.

19 . The system of claim 17 , wherein the at least one image includes a functional image, and the determining feature information of the at least one target region based on at least one parameter of the at least one target region comprises:

obtaining the at least one parameter of the at least one target region in the functional image; and

determining the feature information of the at least one target region based on the at least one parameter of the at least one target region.

20 . A non-transitory computer readable medium, comprising executable instructions that, when executed by at least one processor, direct the at least one processor to perform a method for feature information determination, the method comprising:

obtaining at least one image including a subject, the subject including liver tissue, the at least one image including a first image;

determining a preliminary segmentation result including a preliminary region representative of the liver tissue by inputting the first image into a first segmentation model;

determining a target segmentation result by inputting the preliminary segmentation result into a second segmentation model, the target segmentation result including an effective liver region of the liver tissue, the effective liver region being a region of the liver tissue that excludes a liver lesion region;

determining a target image based on the target segmentation result, the target segmentation result being a 3D image including a plurality of slices of the subject, and the target image being at least one slice selected from the plurality of slices;

determining at least one target region in the target image; and

analyzing the liver tissue of the subject by determining feature information of the at least one target region based on at least one parameter of the at least one target region.

Assignments (1)

Priority Claims (2)

Continuity (1)

References Cited (34)