Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015
Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015
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DOI：
10.1002/mp.12197
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发表时间：
2017-05-01
期刊：
影响因子：
3.8
通讯作者：
Fritscher, Karl D.
中科院分区：
文献类型：
--
作者：
Raudaschl, Patrik F.;Zaffino, Paolo;Fritscher, Karl D.
目的：结构和器官的自动描绘是医学成像的关键步骤。然而，由于结构的大量和多样性以及分割算法的多样性，对于哪种自动分割方法最适合某些应用尚缺乏共识。分割挑战是对分割算法进行公正评估和比较的好方法。方法：在这项工作中，我们描述并展示了 2015 年头颈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) 会议上的卫星活动。六个团队参加了分割 CT 图像头颈区域九个结构的挑战：脑干、下颌骨、视交叉、双侧视神经、双侧腮腺和双侧下颌下腺。 结果：本文使用多个已建立的误差指标和明确的排名系统介绍了本次挑战的定量结果。分析和讨论了不同自动分割方法的优点和缺点。结论：2015 年头颈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。参与团队可以在公正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，明显的趋势是更通用和更少的特定结构的分割算法。 (C) 2017年美国医学物理学家协会
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. (C) 2017 American Association of Physicists in Medicine