US 11,900,592 B2
Method, device, and storage medium for pancreatic mass segmentation, diagnosis, and quantitative patient management
Tianyi Zhao, Bethesda, MD (US); Kai Cao, Shanghai (CN); Ling Zhang, Bethesda, MD (US); Jiawen Yao, Bethesda, MD (US); and Le Lu, Bethesda, MD (US)
Assigned to PING AN TECHNOLOGY (SHENZHEN) CO., LTD., Shenzhen (CN)
Filed by Ping An Technology (Shenzhen) Co., Ltd., Shenzhen (CN)
Filed on Mar. 26, 2021, as Appl. No. 17/213,343.
Claims priority of provisional application 63/120,773, filed on Dec. 3, 2020.
Prior Publication US 2022/0180506 A1, Jun. 9, 2022
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 17/20 (2006.01); G16H 20/00 (2018.01); A61B 5/00 (2006.01)
CPC G06T 7/0012 (2013.01) [A61B 5/425 (2013.01); G06T 7/11 (2017.01); G06T 17/205 (2013.01); G16H 20/00 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for pancreatic mass diagnosis and patient management, comprising:
receiving computed tomography (CT) images of a pancreas of a patient during a multi-phase CT scan, the CT images including a plurality of three-dimensional (3D) images of the pancreas for each phase of the multiple phases and the pancreas of the patient including a mass;
performing a segmentation process on the CT images of the pancreas and the mass to obtain a segmentation mask of the pancreas and the mass of the patient;
performing a mask-to-mesh process on the segmentation mask of the pancreas and the mass of the patient to obtain a mesh model of the pancreas and the mass of the patient;
performing a classification process on the mesh model of the pancreas and the mass of the patient to identify a type and a grade of a segmented pancreatic mass, by performing:
encoding an initialized feature vector hp0 for each vertex p;
feeding the initialized feature vector hp0 into a graph-based residual convolutional network (Graph-ResNet) to perform the classification process under a pixel level, a vertex level, and a global level;
encoding a combined feature vector hvp; and
feeding the combined feature vector hνp into a fully connected global classification layer to identify the type and the grade of the segmented pancreatic mass; and
outputting updated CT images of the pancreas of the patient, the updated CT images including the segmented pancreatic mass highlighted thereon and the type and the grade of the segmented pancreatic mass annotated thereon.