An Efficient Approach for Pancreas Segmentation in Computer Tomography Scans

Pancreatic cancer presents a significant challenge in detection and treatment, with a shallow five-year survival rate. The small size and variable shape of the pancreas make it challenging to identify the presence of cancer in its early stages. To address this problem, we propose a novel method for the semantic segmentation of the pancreas in Computer Tomography (CT) scans, utilizing Convolutional Neural Networks (CNNs). Our approach involves training an encoder-decoder neural network to segment precisely the pancreas in CT images. We conducted experiments on a publicly available dataset, achieving results comparable to state-of-the-art methods based on the average Dice score. Furthermore, we evaluated the impact of different backbone models, providing valuable insights for future optimization. Our findings demonstrate that our approach effectively segments the pancreas in CT scans, potentially improving early detection and treatment planning for pancreatic cancer. This success validates the necessity of developing computer-aided diagnosis tools based on deep learning methods for pancreatic cancer, which are essential to enhancing patient outcomes. In summary, our work provides a solid foundation for developing computer-aided diagnosis tools for pancreatic cancer. Using CNNs for semantic segmentation of the pancreas in CT scans is a promising approach that could significantly improve the early detection and treatment of this deadly disease.