IEDHTrans: A hybrid network with interactive encoders and differential hierarchical transformers for multi-phase breast cancer segmentation

Abstract

Breast cancer, a prevalent malignancy and leading cause of global mortality in women, requires precise tumor assessment. Although multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers high sensitivity for tumor evaluation and treatment monitoring, precise primary tumor segmentation remains challenging, limiting advancements in personalized medicine. Existing segmentation methods struggle with multi-sequence DCE-MRI. Consequently, we propose IEDHTrans, a novel hybrid network leveraging multi-phase DCE-MRI information to enhance breast tumor segmentation. This network comprises an interactive encoders module for accurate multi-phase feature extraction of breast tumor features, a differential hierarchical transformer module to establish global long-distance dependencies on multi-resolution feature graphs, and a convolutional neural network decoders module for feature upsampling. Our method’s effectiveness is validated through quantitative and qualitative experiments on the public MAMA-MIA dataset, the PLHN dataset, and our in-house clinical dataset. This approach consistently outperforms other advanced methods. We achieved dice coefficients of 81.22%, 77.85% and 81.83% on the MAMA-MIA, PLHN dataset and in-house clinical datasets, respectively. The source code and in-house clinical dataset are accessible at https://github.com/WYX-gh/IEDHTrans.