DT-VNet: Deep Transformer-based VNet Framework for 3D Prostate MRI Segmentation

Magnetic Resonance Imaging (MRI) is widely used in examining and diagnosing prostate diseases due to its high resolution. However, the diverse morphology of prostate tissue presents a significant challenge for precise gland segmentation. Convolutional Neural Networks have demonstrated effectiveness in segmenting prostate regions. Nevertheless, their limited capability in extracting global long-range semantic features often leads to unstable network segmentation performance. To address these challenges, we propose a Deep Transformer-based Vnet framework (DT-VNet), which consists of a symmetric encoder-decoder architecture that explores global contextual features and retains local feature information. To effectively learn global and local features, We propose the Deep Union Transformer (DU-Trans) as an encoding base module for capturing comprehensive information. Additionally, we introduce a Pool Fusion Attention (PFA) module for decoding, which emphasizes learning context dependencies and interaction relationships. PFA can also facilitate the fusion of deep and shallow features. To our knowledge, this is the first study about deep transformer-based Vnet framework for prostate segmentation. We validate and compare our method on several public datasets against current state-of-the-art methods. The results demonstrate the superior performance of our proposed method in segmenting 3D prostate MRI.