Recent advancements in patient-specific thoracic surgical planning have highlighted the importance of accurate 3D anatomical segmentation. Deep learning, particularly U-shaped models, has shown robust performance in medical image segmentation. Various attention mechanisms and network configurations have been integrated into these models to enhance accuracy and efficiency. Benchmark studies analyzing the architecture of these models provide valuable insights for clinical deployment. The STUNet model ranked highest in a systematic benchmark study, demonstrating the value of CNN-based U-shaped models for thoracic surgery applications.
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