Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck: Baseline Results
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck : Baseline Results. / Podobnik, Gasper; Ibragimov, Bulat; Strojan, Primoz; Peterlin, Primoz; Vrtovec, Tomaz.
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. s. 1-4.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck
T2 - 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
AU - Podobnik, Gasper
AU - Ibragimov, Bulat
AU - Strojan, Primoz
AU - Peterlin, Primoz
AU - Vrtovec, Tomaz
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - For the head and neck (HaN) cancer, radiotherapy is a mainstay treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional segmentation of OARs from computed tomography (CT) images is required for optimal radiation dose distribution calculation, however, so far there has been no evaluation about the impact of the combined analysis of multiple imaging modalities, such as CT and magnetic resonance (MR). For this purpose, we have devised a database of 56 CT and MR images of the same patients with 31 manually delineated OARs, and in this paper we present the baseline segmentation results that were obtained by applying the nnU-Net framework. The resulting average Dice coefficient of 68% and average 95-percentile Hausdorff distance of 8.2mm on a subset of 14 images indicate that nnU-Net serves as a solid baseline method.
AB - For the head and neck (HaN) cancer, radiotherapy is a mainstay treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional segmentation of OARs from computed tomography (CT) images is required for optimal radiation dose distribution calculation, however, so far there has been no evaluation about the impact of the combined analysis of multiple imaging modalities, such as CT and magnetic resonance (MR). For this purpose, we have devised a database of 56 CT and MR images of the same patients with 31 manually delineated OARs, and in this paper we present the baseline segmentation results that were obtained by applying the nnU-Net framework. The resulting average Dice coefficient of 68% and average 95-percentile Hausdorff distance of 8.2mm on a subset of 14 images indicate that nnU-Net serves as a solid baseline method.
KW - automated segmentation
KW - computed tomography
KW - head and neck radiotherapy planning
KW - magnetic resonance
KW - organs-at-risk
UR - http://www.scopus.com/inward/record.url?scp=85129667946&partnerID=8YFLogxK
U2 - 10.1109/ISBI52829.2022.9761433
DO - 10.1109/ISBI52829.2022.9761433
M3 - Article in proceedings
AN - SCOPUS:85129667946
SP - 1
EP - 4
BT - 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)
PB - IEEE
Y2 - 28 March 2022 through 31 March 2022
ER -
ID: 307747906