Three-dimensional semantic segmentation of pituitary adenomas based on the deep learning framework-nnU-net: A clinical perspective

Abstract

This study developed and evaluated nnU-Net models for three-dimensional semantic segmentation of pituitary adenomas (PAs) from contrast-enhanced T1 (T1ce) images, with aims to train a deep learning-based model cost-effectively and apply it to clinical practice. Methods: This study was conducted in two phases. In phase one, two models were trained with nnUNet using distinct PA datasets. Model 1 was trained with 208 PAs in total, and model 2 was trained with 109 primary nonfunctional pituitary adenomas (NFPA). In phase two, the performances of the two models were investigated according to the Dice similarity coefficient (DSC) in the leave-out test dataset. Results: Both models performed well (DSC > 0.8) for PAs with volumes > 1000 mm3, but unsatisfactorily (DSC < 0.5) for PAs < 1000 mm3. Conclusions: Both nnU-Net models showed good segmentation performance for PAs > 1000 mm3 (75% of the dataset) and limited performance for PAs < 1000 mm3 (25% of the dataset). Model 2 trained with fewer samples was more cost-effective. We propose to combine the use of model-based segmentation for PA > 1000 mm3 and manual segmentation for PA < 1000 mm3 in clinical practice at the current stage.