Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations

Abstract

This article introduces an enhanced teacher–student model featuring a novel Vnet architecture that integrates high-pass and low-pass filters to improve the segmentation of breast magnetic resonance imaging (MRI) images. The model effectively utilizes finely annotated, roughly annotated, and unannotated data to achieve precise breast tissue density segmentation. The teacher–student framework incorporates three specialized Vnet networks, each tailored to different types of annotations. By integrating cosine contrast loss functions between finely and roughly annotated models, and innovatively applying high-pass and low-pass filters within the Vnet architecture, the segmentation performance is significantly enhanced. This hybrid filtering approach enables the model to capture both fine-grained and coarse structural details, leading to more accurate segmentation across various MRI image datasets. Experimental results demonstrate the superiority of the proposed method, achieving Dice values of 0.833 on the finely annotated Shenzhen dataset and 0.780 on the Duke dataset, using 15 finely annotated, 15 roughly annotated, and 58 unlabeled samples provided by Shenzhen People's Hospital. These findings underscore its potential clinical application in breast density assessment.