20.500.12556/RUL-159805 HaN-Seg the head and neck organ-at-risk CT and MR segmentation challenge Background and purpose: To promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge. Materials and methods: The challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD$_{95}$), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test. Results: While 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD$_{95}$ of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities. Conclusion: This challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general. computational challenge segmentation deep learning organs-at-risk computed tomography magnetic resonance radiotherapy head and neck cancer računski izziv segmentacija globoko učenje kritični organi računalniška tomografija magnetna resonanca radioterapija rak glave in vratu true false true Angleški jezik Slovenski jezik Članek v reviji 2024-07-25 13:34:54 2024-07-25 13:34:59 2024-08-30 08:42:12 0000-00-00 00:00:00 2024 0 0 8 str. art. 110410 Vol. 198 Sep. 2024 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.93 1879-0887 10.1016/j.radonc.2024.110410 202738179 1-s2.0-S0167814024006807-main.pdf 1-s2.0-S0167814024006807-main.pdf 1 85AF85F694EF0E1D45E90DB8820CA410 f8affae13f4378bb0c79fef3e4b7cb68b61753e6083daf506950e7e0e3654665 271acb1b-4a7a-11ef-8f74-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=188023 https://www.sciencedirect.com/science/article/pii/S0167814024006807 1 d5cc7292-4a79-11ef-8f74-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=188020 Fakulteta za elektrotehniko 0 0 0