Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction

TL;DR

This paper introduces a novel stage-divided vision-language interaction model with evidential learning for breast tumor segmentation in MRI, effectively utilizing text prompts and uncertainty quantification to improve accuracy in challenging low-contrast and blurred boundary scenarios.

Contribution

The paper proposes a stage-divided vision-language interaction framework combined with evidential learning, enhancing tumor segmentation accuracy and uncertainty estimation in MRI images.

Findings

Outperforms existing segmentation networks on public datasets.

Effectively locates lesions in low contrast and blurred boundary conditions.

Quantifies segmentation uncertainty to improve reliability.

Abstract

Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.