ETDformer: an effective transformer block for segmentation of intracranial hemorrhage

Wanyuan Gong, Yanmin Luo, Fuxing Yang, Huabiao Zhou, Zhongwei Lin, Chi Cai, Youcao Lin, Junyan Chen

Abstract: Intracerebral hemorrhage (ICH) medical image segmentation plays a crucial role in clinical diagnostics and treatment planning. The U-Net architecture, known for its encoder-decoder design and skip connections, is widely used but often struggles with accurately delineating complex struct ures like ICH regions. Recently, transformer models have been incorporated into medical image segmentation, improving performance by capturing long-range dependencies. However, existing methods still face challenges in incorrectly segmenting non-target areas and preserving detailed information in the target region. To address these issues, we propose a novel segmentation model that combines U-Net’s local feature extraction with the transformer’s global perceptiveness. Our method introduces an External Storage Module (ES Module) to capture and store feature similarities between adjacent slices, and a Top-Down Attention (TDAttention) mechanism to focus on relevant lesion regions while enhancing target boundary segmentation. Additionally, we introduce a boundary DoU loss to improve lesion boundary delineation. Evaluations on the intracranial hemorrhage dataset (IHSAH) from the Second Affiliated Hospital of Fujian Medical University, as well as the publicly available Brain Hemorrhage Segmentation Dataset (BHSD), demonstrate that our approach achieves DSC scores of 91.29% and 85.10% on the IHSAH and BHSD datasets, respectively, outperforming the second-best Cascaded MERIT by 2.19% and 2.05%, respectively. Moreover, our method provides enhanced visualization of lesion details, significantly aiding diagnostic accuracy.

External IDs:dblp:journals/mbec/GongLYZLCLC25