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  • Title: Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.
    Author: Raudaschl PF, Zaffino P, Sharp GC, Spadea MF, Chen A, Dawant BM, Albrecht T, Gass T, Langguth C, Lüthi M, Jung F, Knapp O, Wesarg S, Mannion-Haworth R, Bowes M, Ashman A, Guillard G, Brett A, Vincent G, Orbes-Arteaga M, Cárdenas-Peña D, Castellanos-Dominguez G, Aghdasi N, Li Y, Berens A, Moe K, Hannaford B, Schubert R, Fritscher KD.
    Journal: Med Phys; 2017 May; 44(5):2020-2036. PubMed ID: 28273355.
    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.
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