Department of Biomedical Computer Science and Mechatronics, Institute for Biomedical Image Analysis, UMIT, Hall, Tyrol, 6060, Austria.
Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Catanzaro, 88100, Italy.
Med Phys. 2017 May;44(5):2020-2036. doi: 10.1002/mp.12197. Epub 2017 Apr 21.
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.
结构和器官的自动勾画是医学成像的关键步骤。然而，由于结构数量多、种类多，以及分割算法种类繁多，对于哪种自动分割方法最适合某些应用，缺乏共识。分割挑战是对分割算法进行无偏评估和比较的一种很好的方法。
在这项工作中，我们描述并展示了 2015 年头部和颈部自动分割挑战赛的结果，这是 2015 年医学图像计算和计算机辅助干预（MICCAI）会议的一个卫星活动。六个团队参加了一项挑战，旨在对 CT 图像中的九个头部和颈部结构进行分割：脑干、下颌骨、视交叉、双侧视神经、双侧腮腺和双侧颌下腺。
本文使用多种已建立的误差度量标准和明确定义的排名系统，介绍了该挑战赛的定量结果。分析和讨论了不同自动分割方法的优缺点。
2015 年头部和颈部自动分割挑战赛是评估放射治疗风险器官分割当前最新技术的好机会。参赛团队有机会在无偏和标准化的情况下将他们的方法与其他方法进行比较。结果表明，更倾向于使用通用的而不是特定于结构的分割算法。
