HaN-Seg : the head and neck organ-at-risk CT and MR segmentation challenge

Podobnik, Gašper (avtor), Ibragimov, Bulat (avtor), Tappeiner, Elias (avtor), Lee, Chanwoong (avtor), Sung Kim, Jin (avtor), Mesbah, Zacharia (avtor), Modzelewski, Romain (avtor), Ma, Yihao (avtor), Yang, Fan (avtor), Rudecki, Mikołaj (avtor), Wodziński, Marek (avtor), Peterlin, Primož (avtor), Strojan, Primož (avtor), Vrtovec, Tomaž (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: computational challenge, segmentation, deep learning, organs-at-risk, computed tomography, magnetic resonance, radiotherapy, head and neck cancer
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: 8 str.
• Številčenje: Vol. 198, art. 110410
• PID: 20.500.12556/RUL-159805
• UDK: 004.93
• ISSN pri članku: 1879-0887
• DOI: 10.1016/j.radonc.2024.110410
• COBISS.SI-ID: 202738179
• Datum objave v RUL: 25.07.2024

Gradivo je del revije

• Naslov: Radiotherapy & oncology
• Založnik: Elsevier, European Society for Radiotherapy and Oncology
• ISSN: 1879-0887
• COBISS.SI-ID: 23402757

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: računski izziv, segmentacija, globoko učenje, kritični organi, računalniška tomografija, magnetna resonanca, radioterapija, rak glave in vratu