MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets

Abstract

Annotating multiple organs in 3D medical images is time-consuming and costly. Meanwhile, there exist many single-organ datasets with one specific organ annotated. This paper investigates how to learn a multi-organ segmentation model leveraging a set of binary-labeled datasets. A novel Multi-teacher Single-student Knowledge Distillation (MS-KD) framework is proposed, where the teacher models are pre-trained single-organ segmentation networks, and the student model is a multi-organ segmentation network. Considering that each teacher focuses on different organs, a region-based supervision method, consisting of logits-wise supervision and feature-wise supervision, is proposed. Each teacher supervises the student in two regions, the organ region where the teacher is considered as an expert and the background region where all teachers agree. Extensive experiments on three public single-organ datasets and a multi-organ dataset have demonstrated the effectiveness of the proposed MS-KD framework.

References in corpus (8)

• Distilling the Knowledge in a Neural Network
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes
• PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
• Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
• Improving Fast Segmentation With Teacher-student Learning
• Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation
• DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets