ǂthe ǂhead and neck organ-at-risk CT and MR segmentation challenge
Gašper Podobnik (Avtor), Bulat Ibragimov (Avtor), Elias Tappeiner (Avtor), Chanwoong Lee (Avtor), Jin Sung Kim (Avtor), Zacharia Mesbah (Avtor), Primož Strojan (Avtor), Tomaž Vrtovec (Avtor), Romain Modzelewski (Avtor), Yihao Ma (Avtor), Fan Yang (Avtor), Mikołaj Rudecki (Avtor), Marek Wodziński (Avtor), Primož Peterlin (Avtor)

Povzetek

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 (HD95), 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. Thetop-performing team achieved a DSC of 76.9 % and a HD95 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 segmentationperformance surpassing the inter-observer agreement on the same dataset. These results set a benchmark forfuture research on this publicly available dataset and on paired multi-modal image segmentation in general.

Ključne besede

računski izziv;segmentacija;globoko učenje;kritični organi;računalniška tomografija;magnetna resonanca;radioterapija;rak glave in vratu;computational challenge;segmentation;deep learning;organs-at-risk;computed tomography magnetic resonance;radiotherapy;head and neck cancer;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FE - Fakulteta za elektrotehniko
UDK: 004.93
COBISS: 202738179 Povezava se bo odprla v novem oknu
ISSN: 1879-0887
Št. ogledov: 131
Št. prenosov: 40
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: računski izziv;segmentacija;globoko učenje;kritični organi;računalniška tomografija;magnetna resonanca;radioterapija;rak glave in vratu;
Vrsta dela (COBISS): Članek v reviji
Strani: 8 str.
Letnik: ǂVol. ǂ198
Zvezek: ǂ[articel no.] ǂ110410
Čas izdaje: Sep. 2024
DOI: 10.1016/j.radonc.2024.110410
ID: 24599397
Priporočena dela:
, ǂthe ǂhead and neck organ-at-risk CT and MR segmentation challenge
, ǂthe ǂhead and neck organ-at-risk CT & MR segmentation dataset
, diplomsko delo
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