학술논문

Medical Image Segmentation of Liver Tumors with Multi-phase Deficiency Based on Hierarchical Knowledge Distillation Network
Document Type
Conference
Source
2024 43rd Chinese Control Conference (CCC) Chinese Control Conference (CCC), 2024 43rd. :7474-7479 Jul, 2024
Subject
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Knowledge engineering
Image segmentation
Liver cancer
Hospitals
Computed tomography
Liver
Information processing
Liver Cancer Auxiliary Diagnosis
Medical Image Segmentation
Style Matching Module
Context Consistency Module
Hierarchical Knowledge Distillation
Language
ISSN
1934-1768
Abstract
Contrast-enhanced Computed Tomography (CT)-based multi-phase liver tumor medical image segmentation is one of the important methods of liver cancer diagnosis. However, the multiply phases of patients might be incomplete clinically, resulting in the clinical limitation of multi-phase liver tumor medical image segmentation. Recently, the knowledge distillation-based incomplete multi-phase/modal methods conduct the paradigm of teacher-student networks, in which the knowledge of complete modalities are transferred to the student network via teacher network, achieving the contrast-enhanced CT-based incomplete multi-phase liver tumor medical image segmentation. However, such paradigm lacks the effective hierarchical feature representation and the texture information transmission mechanism, resulting in the significant degradation of student network performance against the incomplete modalities. To address this issue, a hierarchical knowledge distillation network (HKD-Net) is proposed to guide the student network to learn the hierarchical knowledge, which consists of teacher-student network, style matching module, context consistency module, hierarchical discriminator, and segmentation consistency module. Experimental results demonstrate the superiority of the proposed method compared with the state-of-the-art incomplete multi-phase/modal medical image segmentation methods, achieving the reliable incomplete multi-phase liver tumor segmentation.