Abstract
Esophageal cancer is a significant global health concern, and radiotherapy (RT) is a common treatment option. Accurate delineation of the gross tumor volume (GTV) is essential for optimal treatment outcomes. In clinical practice, patients may undergo a second round of RT to achieve complete tumor control when the first course of treatment fails to eradicate cancer completely. However, manual delineation is labor-intensive, and automatic segmentation of esophageal GTV is difficult due to the ambiguous boundary of the tumor. Detailed tumor information naturally exists in the previous stage, however the correlation between the first and second course RT is rarely explored. In this study, we first reveal the domain gap between the first and second course RT, and aim to improve the accuracy of GTV delineation in the second course RT by incorporating prior information from the first course. We propose a novel prior Anatomy and RT information enhanced Second-course Esophageal GTV segmentation network (ARTSEG). A region-preserving attention module (RAM) is designed to understand the long-range prior knowledge of the esophageal structure, while preserving the regional patterns. Sparsely labeled medical images for various isolated tasks necessitate efficient utilization of knowledge from relevant datasets and tasks. To achieve this, we train our network in an information-querying manner. ARTSEG incorporates various prior knowledge, including: 1) Tumor volume variation between first and second RT courses, 2) Cancer cell proliferation, and 3) Reliance of GTV on esophageal anatomy. Extensive quantitative and qualitative experiments validate our designs.
H. Liao and X. Yang are the co-corresponding authors.
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Acknowledgments
Thanks to National Key Research and Development Program of China (2022YFC2405200), National Natural Science Foundation of China (82027807, U22A2051), Beijing Municipal Natural Science Foundation (7212202), Institute for Intelligent Healthcare, Tsinghua University (2022ZLB001), Tsinghua-Foshan Innovation Special Fund (2021THFS0104), Guangdong Esophageal Cancer Institute Science and Technology Program (Q202221, Q202214, M-202016).
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Sun, Y. et al. (2023). Second-Course Esophageal Gross Tumor Volume Segmentation in CT with Prior Anatomical and Radiotherapy Information. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_48
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