Multimodal Sensitive Adaptive Transformer for 3D medical image segmentation

Abstract

Three-dimensional medical imaging segmentation presents a significant challenge within the field, with the segmentation of multiple organs and lesions in MRI images being particularly demanding. This paper introduces an innovative approach utilizing the Multimodal Sensitive Adaptive Attention (MSAA). We refer to this new structure as the Multimodal Sensitive Adaptive Transformer Network (MSAT), which incorporates downsampling and Multimodal Sensitive Adaptive Attention into the encoding phase and integrate skip connections from different layers, outputs from Multimodal Sensitive Adaptive Attention, and upsampled feature outputs into the decoding phase. The MSAT consists of two primary components. The initial component is designed to extract a richer set of high-dimensional features through an advanced network architecture. This includes integration of different layers skip connections, outputs from the MSAA, and the results of the preceding upsampling layer. The second component features a Multimodal Sensitive Adaptive Attention block, which integrates two types of attention mechanisms: Local Sensitive Adaptive Attention (LSAA) and Spatial Sensitive Adaptive Attention (SSAA). These attention mechanisms work synergistically to blend high and low-dimensional features effectively, thereby enriching the contextual information captured by the model. Our experiments, conducted across several datasets including Synapse, BTCV, ACDC, and the BraTS 2021 dataset, demonstrate that the MSAT outperforms other existing methodologies. The MSAT shows superior segmentation capabilities for 3D multi-organ, cardiac, and brain tumor segmentation tasks.