Pancreatic cancer, which has a low survival rate, is one of the most challenging cancers to diagnose and treat effectively. Early detection through abdominal computed tomography (CT) scans is crucial, yet complicated by the pancreas' obscure anatomical position, small size, and frequent occlusion by surrounding organs. These factors make the pancreas particularly difficult to identify and segment accurately. While deep learning (DL) models have shown promise for segmentation tasks, their performance still requires significant improvement to address these challenges. In this research, we propose a novel adaptive TverskyCE loss for DL model training, which combines Tversky loss with cross-entropy loss through learnable weights. Our method enables automatic adjustment of loss contributions during training, dynamically optimizing the objective function for improved performance. 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 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
@article{arxiv.2505.01951,
title = {UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation},
author = {Xubei Zhang and Mikhail Y. Shalaginov and Tingying Helen Zeng},
journal= {arXiv preprint arXiv:2505.01951},
year = {2025}
}