UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation

Pancreatic cancer, which has a low survival rate, is the most intractable one among all cancers. Most diagnoses of this cancer heavily depend on abdominal computed tomography (CT) scans. Therefore, pancreas segmentation is crucial but challenging. Because of the obscure position of the pancreas, surrounded by other large organs, and its small area, the pancreas has often been impeded and difficult to detect. With these challenges , the segmentation results based on Deep Learning (DL) models still need to be improved. In this research, we propose a novel adaptive TverskyCE loss for DL model training, which combines Tversky loss with cross-entropy loss using learnable weights. Our method enables the model to adjust the loss contribution automatically and find the best objective function during training. All experiments were conducted on the National Institutes of Health (NIH) Pancreas-CT dataset. We evaluated the adaptive TverskyCE loss on the UNet-3D and Dilated UNet-3D, and our method achieved a Dice Similarity Coefficient (DSC) of 85.59%, with peak performance up to 95.24%, and the score of 85.14%. DSC and the score were improved by 9.47% and 8.98% respectively compared with the baseline UNet-3D with Tversky loss for pancreas segmentation. Keywords: Pancreas segmentation, Tversky loss, Cross-entropy loss, UNet-3D, Dilated UNet-3D