Abstract
Lymphoma treatment planning and prognosis assessment require accurate segmentation of lymphoma lesions. Positron emission tomography (PET) /computed tomography (CT) is widely used for lymphoma segmentation. Many methods do automatic segmentation of lymphoma based on PET/CT. However, a significant challenge that limits the effectiveness of the segmentation method is the large and imbalance variation in size of whole-body lymphoma lesions. For example, a small percentage of images contain large lesions, while most images contain only small lesions or even no lesions, which results in inaccurate segmentation. In this paper, we propose a Multi-task Assisted Network (MTA-Net) for whole-body lymphoma segmentation. First, we design a novel Multi-task Cross-scale Transformer (MCT) block, which combines the pixels regression task and the whole image classification task at multiple scales. Second, we design a Classification Dynamic Convolution (CDC) whose parameters are additionally controlled by the classification task to assist the segmentation task. In our private whole-body lymphoma dataset, experiments show that MTA-Net achieves the best result among state-of-the-art methods on Dice, HD (Hausdorff Distance), Recall, and Precision.
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This work was supported by the National Natural Science Foundation of China under Grant 62088102 and Central Guidance on Local Science and Technology Development Fund 2022ZY1-CGZY-01HZ02.
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Liang, Z. et al. (2024). MTA-Net: A Multi-task Assisted Network for Whole-Body Lymphoma Segmentation. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 711. Springer, Cham. https://doi.org/10.1007/978-3-031-63211-2_14
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