DUR-Net+: Semi-Supervised Abdominal CT Pheochromocytoma Segmentation Via Dynamic Uncertainty Rectified and Prior Knowledge From SAM-Med3D

Pheochromocytoma is a rare urological adrenal tumor disease. Automated segmentation of pheochromocytomas from computed tomography (CT) is essential for diagnosis and treatment. However, this task is a challenging one due to issues such as blurred boundaries, irregular shapes, variations in location and size, and the lack of annotated images for training. To address these issues, we propose a semi-supervised framework for pheochromocytoma segmentation that primarily consists of a dynamic uncertainty rectification mechanism and a supervised strategy based on SAM-Med3D prior knowledge. First, we design a semi-supervised segmentation model comprising a shared encoder and multiple independent decoders that dynamically select pseudo labels from the different decoder outputs. To mitigate the risk of unreliable predictions caused by sparse annotations during training, we introduce uncertainty estimation to prioritize reliable outputs. Additionally, an Attentional Convolution Block (ACB) is designed in the encoding stage to fully utilize both global and local features, improving tumor recognition in segmentation. Furthermore, SAM-Med3D prior knowledge is incorporated into the framework as supplementary supervisory information, aiding the model in learning from limited labeled data. To eliminate the labor-intensive requirement for manual prompts in SAM-Med3D, we leverage pseudo labels to generate high-quality mask prompts, thus transforming the clinical workflow. Experiments on two pheochromocytoma datasets from different centers demonstrate that our proposed method achieves competitive performance.