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The 2014 liver ultrasound tracking benchmark

The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included...

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Autores principales: De Luca, V, Benz, T, Kondo, S, König, L, Lübke, D, Rothlübbers, S, Somphone, O, Allaire, S, Lediju Bell, M A, Chung, D Y F, Cifor, A, Grozea, C, Günther, M, Jenne, J, Kipshagen, T, Kowarschik, M, Navab, N, Rühaak, J, Schwaab, J, Tanner, C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5454593/
https://www.ncbi.nlm.nih.gov/pubmed/26134417
http://dx.doi.org/10.1088/0031-9155/60/14/5571
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author De Luca, V
Benz, T
Kondo, S
König, L
Lübke, D
Rothlübbers, S
Somphone, O
Allaire, S
Lediju Bell, M A
Chung, D Y F
Cifor, A
Grozea, C
Günther, M
Jenne, J
Kipshagen, T
Kowarschik, M
Navab, N
Rühaak, J
Schwaab, J
Tanner, C
author_facet De Luca, V
Benz, T
Kondo, S
König, L
Lübke, D
Rothlübbers, S
Somphone, O
Allaire, S
Lediju Bell, M A
Chung, D Y F
Cifor, A
Grozea, C
Günther, M
Jenne, J
Kipshagen, T
Kowarschik, M
Navab, N
Rühaak, J
Schwaab, J
Tanner, C
author_sort De Luca, V
collection PubMed
description The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included 54 2D and 3D sequences of the liver of healthy volunteers and tumor patients under free breathing. Participants had to provide the tracking results of 90% of the data (test set) for pre-defined point-landmarks (healthy volunteers) or for tumor segmentations (patient data). In this paper we compare the best six methods which participated in the challenge. Quantitative evaluation was performed by the organizers with respect to manual annotations. Results of all methods showed a mean tracking error ranging between 1.4 mm and 2.1 mm for 2D points, and between 2.6 mm and 4.6 mm for 3D points. Fusing all automatic results by considering the median tracking results, improved the mean error to 1.2 mm (2D) and 2.5 mm (3D). For all methods, the performance is still not comparable to human inter-rater variability, with a mean tracking error of 0.5–0.6 mm (2D) and 1.2–1.8 mm (3D). The segmentation task was fulfilled only by one participant, resulting in a Dice coefficient ranging from 76.7% to 92.3%. The CLUST database continues to be available and the online leader-board will be updated as an ongoing challenge.
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spelling pubmed-54545932017-06-30 The 2014 liver ultrasound tracking benchmark De Luca, V Benz, T Kondo, S König, L Lübke, D Rothlübbers, S Somphone, O Allaire, S Lediju Bell, M A Chung, D Y F Cifor, A Grozea, C Günther, M Jenne, J Kipshagen, T Kowarschik, M Navab, N Rühaak, J Schwaab, J Tanner, C Phys Med Biol Paper The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included 54 2D and 3D sequences of the liver of healthy volunteers and tumor patients under free breathing. Participants had to provide the tracking results of 90% of the data (test set) for pre-defined point-landmarks (healthy volunteers) or for tumor segmentations (patient data). In this paper we compare the best six methods which participated in the challenge. Quantitative evaluation was performed by the organizers with respect to manual annotations. Results of all methods showed a mean tracking error ranging between 1.4 mm and 2.1 mm for 2D points, and between 2.6 mm and 4.6 mm for 3D points. Fusing all automatic results by considering the median tracking results, improved the mean error to 1.2 mm (2D) and 2.5 mm (3D). For all methods, the performance is still not comparable to human inter-rater variability, with a mean tracking error of 0.5–0.6 mm (2D) and 1.2–1.8 mm (3D). The segmentation task was fulfilled only by one participant, resulting in a Dice coefficient ranging from 76.7% to 92.3%. The CLUST database continues to be available and the online leader-board will be updated as an ongoing challenge. IOP Publishing 2015-07-21 2015-07-02 /pmc/articles/PMC5454593/ /pubmed/26134417 http://dx.doi.org/10.1088/0031-9155/60/14/5571 Text en © 2015 Institute of Physics and Engineering in Medicine http://creativecommons.org/licenses/by/3.0/ Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
De Luca, V
Benz, T
Kondo, S
König, L
Lübke, D
Rothlübbers, S
Somphone, O
Allaire, S
Lediju Bell, M A
Chung, D Y F
Cifor, A
Grozea, C
Günther, M
Jenne, J
Kipshagen, T
Kowarschik, M
Navab, N
Rühaak, J
Schwaab, J
Tanner, C
The 2014 liver ultrasound tracking benchmark
title The 2014 liver ultrasound tracking benchmark
title_full The 2014 liver ultrasound tracking benchmark
title_fullStr The 2014 liver ultrasound tracking benchmark
title_full_unstemmed The 2014 liver ultrasound tracking benchmark
title_short The 2014 liver ultrasound tracking benchmark
title_sort 2014 liver ultrasound tracking benchmark
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5454593/
https://www.ncbi.nlm.nih.gov/pubmed/26134417
http://dx.doi.org/10.1088/0031-9155/60/14/5571
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