PheoSeg: A 3D transfer learning framework for accurate abdominal CT pheochromocytoma segmentation and surgical grade prediction

Abstract

Pheochromocytoma is a rare urological disease. Accurate segmentation of pheochromocytoma and its surrounding abdominal organs is crucial for surgical planning and preoperative diagnosis. However, current clinical datasets typically include tumor labels but lack comprehensive multi-organ abdominal labels, and detailed descriptions of the spatial relationships between the tumor and adjacent organs are insufficient. To address the challenge of automatic multi-organ segmentation in the abdomen without comprehensive labels around pheochromocytoma, we propose a novel method for automatic multi-source migration segmentation and preoperative diagnostic grading of pheochromocytoma and its surrounding organs. This method utilizes nnUNet to extract data fingerprint information from the target domain (Clinical Pheochromocytoma dataset of Peking Union Medical College Hospital, PPGL) as prior knowledge, which is then transferred to two source domains (FLARE2022 and AMOS2022). We trained 10 models and incorporated them into a "Model Bank". Additionally, we employed an improved CC-FV method to screen the models in the "Model Bank," identifying the model with the highest mobility (class consistency and feature diversity). This selected model served as a pre-trained model for abdominal multi-organ segmentation and was subsequently adapted for tumor segmentation. To address the issue of limited clinical data, we introduced a data enhancement technique for 3D medical images. The enhanced dataset was used to fine-tune the pre-trained model, resulting in a model capable of segmenting multiple organs and tumors in the abdomen. From the segmentation results, we extracted the spatial relationships between the tumor and surrounding abdominal organs and constructed a radiomic model to predict and analyze the surgical grading. Experimental results demonstrate that our proposed method achieves a Dice coefficient that is, on average, 10.18 % higher on FLARE2022 and AMOS2022 datasets compared to segmentation frameworks such as Swin-Unet, UNETR, CoTr, nnformer, and nnUNetv2. For the PPGL dataset, the Dice coefficient for pheochromocytoma and surrounding organs was 87.27 %. In summary, our method effectively realizes efficient and automatic segmentation of pheochromocytoma and surrounding organs, as well as preoperative diagnostic classification.