Abstract
Machine learning, especially deep learning, has achieved state-of-the-art performance on various computer vision tasks. For computer vision tasks in the medical domain, it remains as challenging tasks since medical data is heterogeneous, multi-level, and multi-scale. Head and Neck Tumor Segmentation Challenge (HECKTOR) provides a platform to apply machine learning techniques to the medical image domain. HECKTOR 2022 provides positron emission tomography/computed tomography (PET/CT) images which includes useful metabolic and anatomical information to sufficiently make an accurate tumor segmentation. In this paper, we proposed a stacked-multi-scaled medical image segmentation framework to automatically segment the Head and Neck tumor using PET/CT images. The main idea of our network was to generate various low-resolution feature maps of PET/CT images to make a better contour of Head and Neck tumors. We used multi-scaled PET/CT images as inputs, and stacked different intermediate feature maps by resolution for a better inference result. In addition, we evaluated our model on the HECKTOR challenge test dataset. Overall, we achieved a 0.69786, 0.66730 mean Dice score on GTVp and GTVn respectively. Our team’s name is HPCAS.
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Acknowledgment
We acknowledge support from National Science Foundation under the Grant 2015254, from the University of Texas at Anderson Cancer Center, Texas Advanced Computing Center, and Oden Institute for Computational and Engineering Sciences initiative in Oncological Data and Computational Science.
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Shi, Y., Zhang, X., Yan, Y. (2023). Stacking Feature Maps of Multi-scaled Medical Images in U-Net for 3D Head and Neck Tumor Segmentation. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626. Springer, Cham. https://doi.org/10.1007/978-3-031-27420-6_8
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