Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015
| Autor: | Angelique M. Berens, Christoph Langguth, Annaliese Ashman, Marcel Lüthi, Oliver Knapp, Florian Jung, Kris S. Moe, Yangming Li, Mauricio Orbes-Arteaga, Stefan Wesarg, Thomas Albrecht, Antong Chen, Tobias Gass, Rainer Schubert, Karl D. Fritscher, Paolo Zaffino, Nava Aghdasi, Maria Francesca Spadea, Germán Castellanos-Domínguez, Gregory C. Sharp, G.R. Vincent, Alan Brett, Richard Mannion-Haworth, Benoit M. Dawant, David Cárdenas-Peña, Gwenael Guillard, Blake Hannaford, Patrik Raudaschl, Michael A. Bowes |
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| Přispěvatelé: | Publica |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
030218 nuclear medicine & medical imaging 03 medical and health sciences Segmentation 0302 clinical medicine Medical imaging Humans atlas-based segmentation Head and neck Auto segmentation business.industry Pattern recognition Model based segmentations Individual Health General Medicine segmentation challenge Human computer interaction (HCI) Head and Neck Neoplasms 030220 oncology & carcinogenesis Automatic segmentation Artificial intelligence Tomography X-Ray Computed business Head Algorithms Neck |
| Zdroj: | Medical Physics. 44:2020-2036 |
| ISSN: | 0094-2405 |
| DOI: | 10.1002/mp.12197 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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