DAMNet: A Network Based on Dual Attention and Multi-Resolution Inputs for the Segmentation of Thoracic and Abdominal Organs

Segmentation of thoracic and abdominal organs is crucial for accurate disease diagnosis, surgical planning, and long-term health management of patients. Deep learning often depends on large quantities of high-quality training data to achieve superior results. However, the inherent complexity and sparsity of medical images require advanced models with greater learning capabilities. Additionally, significant morphological differences between organs often result in inaccurate and false-positive segmentation. To alleviate these issues, we propose a Dual Attention (DA) and Multi-Resolution Inputs (MI) based network (DAMNet) for the segmentation of thoracic and abdominal organs. Specially, DAMNet utilizes a single encoder and dual decoders. The encoder integrates MI with the Transformer to achieve multi-level feature fusion and capture global image relationships, improving the model’s capability to process intricate image data. In the decoders, Residual U-blocks (RSU) and the DA module consisting of Spatial Multi-Scale Cross-Axis Attention (SMCA) and Convolutional Self-Attention (CSA) are used as two decoder branches, respectively. The design of decoders allows the model to extract detailed information from different encoding layers from two perspectives, thereby reducing inaccuracies in segmentation. We perform thorough experiments and evaluations using three publicly available datasets: Synapse, SegTHOR, and THoracic. The experimental results indicate that our proposed DAMNet model demonstrates exceptional proficiency in segmenting thoracic and abdominal organs.