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Evaluation of Segmentation Methods on Head and Neck CT: Auto-Segmentation Challenge 2015

Raudaschl, P. F. and Zaffino, P. and Sharp, G. C. and Spadea, M. F. and Chen, A. and Dawant, B. M. and Albrecht, T. and Gass, T. and Langguth, C. and Lüthi, M. and Jung, F. and Knapp, O. and Wesarg, S. and Mannion-Haworth, R. and Bowes, M. and Ashman, A. and Guillard, G. and Brett, A. and Vincent, G. and Orbes-Arteaga, M. and Cárdenas-Peña, D. and Castellanos-Dominguez, G. and Aghdasi, N. and Li, Y. and Berens, A. and Moe, K. and Hannaford, B. and Schubert, R. and Fritscher, K. D.. (2017) Evaluation of Segmentation Methods on Head and Neck CT: Auto-Segmentation Challenge 2015. Medical Physics, 44 (5). pp. 2020-2036.

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Official URL: https://edoc.unibas.ch/59211/

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Abstract

Purpose: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms.
Methods: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands.
Results: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed.
Conclusions: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Lüthi, Marcel and Vetter, Thomas
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Wiley
ISSN:0094-2405
e-ISSN:2473-4209
Note:Publication type according to Uni Basel Research Database: Journal article -- The final publication is available at Wiley, see Doi link.
Language:English
Identification Number:
Last Modified:09 Feb 2018 12:51
Deposited On:09 Feb 2018 12:51

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