MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets
This work addresses the costly annotation challenge in medical imaging for researchers and practitioners, though it is incremental as it builds on existing knowledge distillation methods.
The paper tackles the problem of multi-organ segmentation in 3D medical images by leveraging multiple binary-labeled datasets, proposing a Multi-teacher Single-student Knowledge Distillation (MS-KD) framework that achieves effective results as demonstrated in experiments on public datasets.
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.