Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation

TL;DR

This paper reviews recent semi-supervised learning methods for medical image segmentation, highlighting their technical innovations, empirical results, and existing limitations to inspire future research in the field.

Contribution

It provides a comprehensive survey of semi-supervised techniques in medical image segmentation, summarizing key methods, results, and identifying unresolved challenges.

Findings

Semi-supervised methods improve segmentation with limited annotations.

Recent approaches show promising empirical results.

Several limitations and open problems remain in current methods.

Abstract

Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarized both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review could inspire the research community to explore solutions for this challenge and further promote the developments in medical image segmentation field.