Efficient Medical Image Segmentation Based on Knowledge Distillation

TL;DR

This paper introduces a knowledge distillation approach to create efficient, lightweight medical image segmentation networks that maintain high accuracy and are suitable for real-world applications with limited computational resources.

Contribution

It proposes a novel distillation module tailored for medical segmentation, improving lightweight network performance by transferring semantic region information.

Findings

Lightweight network improves up to 32.6% in segmentation accuracy.

The method reduces computational complexity and storage requirements.

Validated on public CT datasets LiTS17 and KiTS19.

Abstract

Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity and massive storage, which is impractical in the real-world scenario. To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network. This architecture empowers the lightweight network to get a significant improvement on segmentation capability while retaining its runtime efficiency. We further devise a novel distillation module tailored for medical image segmentation to transfer semantic region information from teacher to student network. It forces the student network to mimic the extent of difference of representations calculated from different tissue regions. This module avoids the ambiguous boundary problem encountered when dealing with medical imaging but instead encodes the internal information of each semantic region for transferring. Benefited from our module, the lightweight network could receive an improvement of up to 32.6% in our experiment while maintaining its portability in the inference phase. The entire structure has been verified on two widely accepted public CT datasets LiTS17 and KiTS19. We demonstrate that a lightweight network distilled by our method has non-negligible value in the scenario which requires relatively high operating speed and low storage usage.