With a 5-year survival rate of only 9%, pancreatic cancer is one of the deadliest types of cancer. Among other things, this is caused by the extreme difficulty of diagnosing recurrent pancreatic cancer in an early stage. One of the important next steps in discovering pancreatic c
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With a 5-year survival rate of only 9%, pancreatic cancer is one of the deadliest types of cancer. Among other things, this is caused by the extreme difficulty of diagnosing recurrent pancreatic cancer in an early stage. One of the important next steps in discovering pancreatic cancer automatically on a CT scan is finding healthy pancreatic tissue. This research explores the feasibility of segmenting the pancreas on a CT scan using a deep learning approach, focusing on postoperative cases after pancre- atic resection, by combining state-of-the-art segmentation models for the preoperative pancreas with novel techniques, such as 3D Multi-Scale Convolutional Blocks (MCBs) and KNet architectures. Us- ing pretraining on preoperative data, a complete segmentation pipeline was designed to segment the pancreas in the postoperative state. The experimental results demonstrate that deep learning models can effectively segment the pancreas, despite the increased complexity and difficulty of the postoper- ative state. Notably, employing advanced techniques such as the KNet architecture and MCBs yields significant performance improvements. The newly designed architectures presented in this research, MKNet, MSKNet, and MAKNet, achieve state-of-the-art results for both preoperative and postoperative pancreas segmentation, greatly improving the Hausdorff Distance (HD) and 95th Percentile Hausdorff Distance (HD95) in particular. In the preoperative state, substantial performance improvements were observed compared to the previous state-of-the-art, with a 12.17% increase for HD and a 14.64% in- crease for HD95. Similarly, in the postoperative state, improvements of 11.99% for HD and 13.25% for HD95 were measured. Additionally, qualitative evaluations were conducted with seven experienced radiologists and radiotherapists to validate the algorithm’s performance in a clinical context. Remark- ably, in 83% of the cases, the algorithm was evaluated to accurately segment the pancreas, requiring minimal or no modifications according to the medical experts. This study not only contributes to the state-of-the-art in pancreas segmentation but also introduces comprehensive quantitative and qualitative evaluation methodologies. Furthermore, it addresses the segmentation of the postoperative pancreas, which has not yet been touched upon in previous re- search. Limitations are acknowledged, such as the availability of a limited dataset, and future research directions are proposed, including generalizability and enhancements to the proposed models. Despite the limited availability of data, which impacts the performance and generalization capabilities to some degree, the research in this thesis showcases the capability of the MKNet family architectures to ac- curately segment the postoperative pancreas, offering potential benefits for medical applications, data annotation acceleration, and future research in this domain.
