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HaN-Seg : the head and neck organ-at-risk CT and MR segmentation challenge
ID
Podobnik, Gašper
(
Author
),
ID
Ibragimov, Bulat
(
Author
),
ID
Tappeiner, Elias
(
Author
),
ID
Lee, Chanwoong
(
Author
),
ID
Sung Kim, Jin
(
Author
),
ID
Mesbah, Zacharia
(
Author
),
ID
Modzelewski, Romain
(
Author
),
ID
Ma, Yihao
(
Author
),
ID
Yang, Fan
(
Author
),
ID
Rudecki, Mikołaj
(
Author
),
ID
Wodziński, Marek
(
Author
),
ID
Peterlin, Primož
(
Author
),
ID
Strojan, Primož
(
Author
),
ID
Vrtovec, Tomaž
(
Author
)
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https://www.sciencedirect.com/science/article/pii/S0167814024006807
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Abstract
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.
Language:
English
Keywords:
computational challenge
,
segmentation
,
deep learning
,
organs-at-risk
,
computed tomography
,
magnetic resonance
,
radiotherapy
,
head and neck cancer
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FE - Faculty of Electrical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2024
Number of pages:
8 str.
Numbering:
Vol. 198, art. 110410
PID:
20.500.12556/RUL-159805
UDC:
004.93
ISSN on article:
1879-0887
DOI:
10.1016/j.radonc.2024.110410
COBISS.SI-ID:
202738179
Publication date in RUL:
25.07.2024
Views:
403
Downloads:
74
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Record is a part of a journal
Title:
Radiotherapy & oncology
Publisher:
Elsevier, European Society for Radiotherapy and Oncology
ISSN:
1879-0887
COBISS.SI-ID:
23402757
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Secondary language
Language:
Slovenian
Keywords:
računski izziv
,
segmentacija
,
globoko učenje
,
kritični organi
,
računalniška tomografija
,
magnetna resonanca
,
radioterapija
,
rak glave in vratu
Projects
Funder:
ARIS - Slovenian Research and Innovation Agency
Project number:
J2-1732
Name:
Računalniško podprta analiza medicinskih slik za načrtovanje zdravljenja s protonsko in radioterapijo
Funder:
ARIS - Slovenian Research and Innovation Agency
Project number:
P2-0232
Name:
Analiza biomedicinskih slik in signalov
Funder:
ARIS - Slovenian Research and Innovation Agency
Project number:
P3-0307
Name:
Rak glave in vratu - analiza bioloških značilnosti in poskus izboljšanja zdravljenja
Funder:
Other - Other funder or multiple funders
Funding programme:
Novo Nordisk Foundation
Project number:
NFF20OC0062056
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