AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis - Yang, Minqiang; Zhang, Yuhong; Chen, Haoning; Wang, Wei; Ni, Haixu; Chen, Xinlong; Li, Zhuoheng; Mao, Chengsheng (Frontiers in Oncology, 2022)
Image segmentation plays an essential role in medical imaging analysis such as tumor boundary extraction. Recently, Deep learning techniques have dramatically improved performance for image segmentation. However, an important factor preventing deep neural networks from going further is the information loss during the information propagation process. In this article, we present AX-Unet, a deep learning framework incorporating a modified Atrous Spatial Pyramid Pooling (ASPP) module to learn the location information and to extract multi-level contextual information to reduce information loss during down-sampling. We also introduce a special group convolution operation on the feature map at each level to achieve information decoupling between channels. In addition, we propose an explicit boundary-aware loss function to tackle the the blurry boundary problem. We evaluate our model on two public Pancreas-CT datasets, NIH Pancreas-CT dataset and the pancreas part in MSD medical dataset. The experimental results validate that our model can outperform the state-of-the-art methods in pancreas CT image segmentation. By comparing the extracted the feature output of our model, we find that the pancreatic region of normal people and patients with pancreatic tumors show significant difference. This could provide a promising and reliable way to assist physicians for the screening of pancreatic tumors.