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

: Raudaschl, Patrik F.; Zaffino, Paolo; Sharp, Gregory C.; Spadea, Maria Francesca; Chen, Antong; Dawant, Benoit M.; Albrecht, Thomas; Gass, Tobias; Langgut, Christoph; Lüthi, Marcel; Jung, Florian; Knapp, Oliver; Wesarg, Stefan; Mannion-Haworth, Richard; Bowes, Mike; Ashman, Annaliese; Guillard, Gwenael; Brett, Alan; Vincent, Graham; Orbes-Arteaga, Mauricio; Cárdenas-Peña, David; Castellanos-Dominguez, German; Aghdasi, Nava; Li, Yangming; Berens, Angelique; Moe, Kris; Hannaford, Blake; Schubert, Rainer; Fritscher, Karl D.


Medical physics 44 (2017), No.5, pp.2020-2036
ISSN: 0094-2405
Journal Article
Fraunhofer IGD ()
Automatic segmentation; Model based segmentations; Segmentation; Medical imaging; Individual Health; Human computer interaction (HCI); atlas-based segmentation; segmentation challenge

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.

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.

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.

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.