Belief function-based semi-supervised learning for brain tumor segmentation

TL;DR

This paper introduces a belief function-based semi-supervised neural network that improves brain tumor segmentation accuracy by handling uncertain boundaries and limited annotated data, outperforming existing methods.

Contribution

It presents a novel evidential neural network with information fusion for uncertain boundary handling and semi-supervised learning for data scarcity in medical image segmentation.

Findings

Outperforms state-of-the-art segmentation methods

Effectively manages uncertain lesion boundaries

Enhances segmentation accuracy with limited annotations

Abstract

Precise segmentation of a lesion area is important for optimizing its treatment. Deep learning makes it possible to detect and segment a lesion field using annotated data. However, obtaining precisely annotated data is very challenging in the medical domain. Moreover, labeling uncertainty and imprecision make segmentation results unreliable. In this paper, we address the uncertain boundary problem by a new evidential neural network with an information fusion strategy, and the scarcity of annotated data by semi-supervised learning. Experimental results show that our proposal has better performance than state-of-the-art methods.