Harnessing Medical Imaging Applications: Survey on 3D Deep Learning Models for Image Segmentation

The domain of 3D medical image segmentation has progressed markedly with the incorporation of deep learning methodologies, offering critical instruments for accurate and quick examination of intricate medical pictures. Various research has created 3D convolutional neural networks (CNNs) designed to segment diverse anatomical and pathological features, tackling specific issues such as high dimensionality, class imbalance, and computing requirements. Innovative models such as DeepMedic, which implemented a multiresolution strategy for brain lesion identification, together with other U-Net adaptations, have attained remarkable outcomes in tumor and organ segmentation through the integration of contextual and spatial data. Recent advancements encompass models employing large-kernel (LK) attention modules, transformer-based architectures, and hybrid approaches such as UNETR++ with Efficient Paired Attention, all enhancing accuracy while optimizing computing efficiency. These developments highlight the potential of 3D deep learning for clinical applications; yet, challenges such as restricted data availability, substantial annotation expenses, and generalizability across varied datasets persist as avenues for additional investigation.