DP-Net: A 3D Dilated Projection Framework For Precise Fetal Brain Tissue Segmentation

Precise segmentation of fetal brain tissues in MRI is essential for studying brain development and for the early diagnosis and treatment of neurological disorders. However, the complex and variable anatomy of the fetal brain, significant morphological changes at different gestational ages, and the low-quality MRI and inherent noise of fetal acquisition pose significant challenges. To address these, we propose a novel 3D Dilated Projection U-net segmentation framework, DP-Net, which incorporates large kernel convolutions, atrous convolution for receptive field expansion, and dual skip connections mechanism to enhance global semantic consistency. Specifically, we introduce a Dilated Projection Block (DPB) that leverages atrous convolution to capture global context across multiple anatomical regions without additional parameters. Furthermore, we propose a Dual Skip Connection (DSC) mechanism to maintain encoder-decoder global consistency by fusing low-level and projected high-level features, mitigating blind spots introduced by atrous convolution. Extensive experiments show that our method significantly outperforms state-of-the-art methods, demonstrating its robustness and effectiveness in addressing the challenges of fetal brain tissue segmentation.