Semi-supervised Medical Image Segmentation via Perturbation-Aware Mutual Learning and Edge-Aware Uncertainty Loss for Accurate Anatomical Delineation.

Accurate segmentation of medical volumes in magnetic resonance imaging is essential for the exploration of organ structures. Despite the impressive performance of supervised learning in medical image segmentation, its reliance on the large availability of labeled datasets poses a challenge due to the expertise and effort required for data acquisition. Semi-supervised learning (SSL) methods extract additional information from unlabeled datasets without involvement of expert annotation, and many approaches have been proposed to assist with this task. However, they often yield satisfactory results in the central regions but do not perform well in the edge regions due to low contrast edges and ambiguous tissue intensities at the boundary. Meanwhile, standard loss functions are biased towards large regions and cause under-penalizing of boundary errors. In this paper, we propose a novel framework that effectively leverages unlabeled data to improve segmentation performance in cardiac structures. Firstly, we design a mutual learning module with multiple decoders to obtain different predicted probabilities through feature perturbations. Secondly, we apply a novel consistency constraint between labeled and unlabeled data by a dual fine-grained boundary loss that provide global characteristics-based guidance from the transition of the boundary region and an edge-aware uncertainty loss. We evaluate the proposed framework on two publicly available cardiac datasets, including the automated cardiac diagnosis challenge (ACDC) dataset under a 2D setting and the left atrium (LA) dataset under a 3D setting, and compare it with seven recent state-of-the-art semi-supervised methods. Through extensive qualitative visualization and quantitative experiments under standard semi-supervised settings, we demonstrate the effectiveness of the proposed approach and its superior segmentation performance compared with existing methods. Furthermore, clinically relevant cardiac measurements derived from the segmentation results are evaluated to analyze the clinical relevance of the proposed framework. To support reproducibility and future research, our code is publicly available at: https://github.com/waqasanwaar/SSL4MIS_PMEUL .