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Evaluation of segmentation methods on head and neck CT : Auto‐segmentation challenge 2015
Author(s) -
Raudaschl Patrik F.,
Zaffino Paolo,
Sharp Gregory C.,
Spadea Maria Francesca,
Chen Antong,
Dawant Benoit M.,
Albrecht Thomas,
Gass Tobias,
Langguth Christoph,
Lüthi Marcel,
Jung Florian,
Knapp Oliver,
Wesarg Stefan,
MannionHaworth Richard,
Bowes Mike,
Ashman Annaliese,
Guillard Gwenael,
Brett Alan,
Vincent Graham,
OrbesArteaga Mauricio,
CárdenasPeña David,
CastellanosDominguez German,
Aghdasi Nava,
Li Yangming,
Berens Angelique,
Moe Kris,
Hannaford Blake,
Schubert Rainer,
Fritscher Karl D.
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12197
Subject(s) - segmentation , computer science , image segmentation , artificial intelligence , medical imaging , head and neck , medicine , surgery
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.