Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation

TL;DR

This paper reviews various learning paradigms for multi-organ segmentation in medical images, emphasizing approaches that address the challenge of scarce annotations through transfer, semi-supervised, and partially-supervised learning.

Contribution

It provides a comprehensive systematic review of annotation-efficient learning paradigms specifically applied to multi-organ segmentation.

Findings

Transfer learning leverages external datasets effectively.

Semi-supervised learning utilizes unannotated data to improve segmentation.

Partially-supervised learning handles partially-labeled datasets to enhance model performance.

Abstract

Precise delineation of multiple organs or abnormal regions in the human body from medical images plays an essential role in computer-aided diagnosis, surgical simulation, image-guided interventions, and especially in radiotherapy treatment planning. Thus, it is of great significance to explore automatic segmentation approaches, among which deep learning-based approaches have evolved rapidly and witnessed remarkable progress in multi-organ segmentation. However, obtaining an appropriately sized and fine-grained annotated dataset of multiple organs is extremely hard and expensive. Such scarce annotation limits the development of high-performance multi-organ segmentation models but promotes many annotation-efficient learning paradigms. Among these, studies on transfer learning leveraging external datasets, semi-supervised learning using unannotated datasets and partially-supervised learning integrating partially-labeled datasets have led the dominant way to break such dilemma in multi-organ segmentation. We first review the traditional fully supervised method, then present a comprehensive and systematic elaboration of the 3 abovementioned learning paradigms in the context of multi-organ segmentation from both technical and methodological perspectives, and finally summarize their challenges and future trends.