Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.
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DOI：
10.1016/j.media.2013.12.002
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发表时间：
2014-02
影响因子：
10.9
通讯作者：
Madabhushi, Anant
中科院分区：
文献类型：
--
作者：
Litjens, Geert;Toth, Robert;van de Ven, Wendy;Hoeks, Caroline;Kerkstra, Sjoerd;van Ginneken, Bram;Vincent, Graham;Guillard, Gwenael;Birbeck, Neil;Zhang, Jindang;Strand, Robin;Malmberg, Filip;Ou, Yangming;Davatzikos, Christos;Kirschner, Matthias;Jung, Florian;Yuan, Jing;Qiu, Wu;Gao, Qinquan;Edwards, Philip Eddie;Maan, Bianca;van der Heijden, Ferdinand;Ghose, Soumya;Mitra, Jhimli;Dowling, Jason;Barratt, Dean;Huisman, Henkjan;Madabhushi, Anant
前列腺MRI图像分割一直是一个激烈的研究领域，由于越来越多地使用MRI作为前列腺癌的临床检查模式。分割对于各种任务是有用的，例如，为了放射治疗准确地定位前列腺边界或初始化多模式配准算法。过去，研究小组很难在多中心、多供应商和多协议数据上评估前列腺分割算法。特别是因为我们处理的是MR图像，图像外观、分辨率和伪影的存在受到扫描仪和/或协议差异的影响，这反过来又会对算法准确性产生很大影响。设置了前列腺MR图像分割（PROMISE 12）挑战，以便在性能和稳健性的基础上对分割方法进行公平和有意义的比较。在这项工作中，我们将讨论在线PROMISE 12挑战的初步结果，以及在MICCAI 2012会议主办的现场挑战研讨会上获得的结果。在挑战中，纳入了来自4个不同中心的100例前列腺MR病例，扫描仪制造商、场强和方案存在差异。共有来自学术研究团体和工业界的11个团队参加。算法在方法和实现上表现出广泛的多样性，包括主动外观模型、图谱配准和水平集。使用基于边界和体积的度量进行评估，这些度量被组合成将度量与人类专家性能相关的单个分数。挑战的获胜者是Imorphics和ScrAutoProstate团队的算法，总得分为85.72和84.29。这两种算法在挑战中显著优于所有其他算法（p < 0.05），并且具有有效的实现，每种情况的运行时间分别为8分钟和3秒。总的来说，基于主动外观模型的方法似乎在精度和计算时间方面优于其他方法，如多图谱配准。虽然平均算法性能是好到优秀的，Imorphics算法平均优于第二个观察者，我们表明，算法组合可能会导致进一步的改善，这表明前列腺分割的最佳性能尚未获得。所有结果均可在http://promise12.grand-challenge.org/网站上查阅。
Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 minutes and 3 second per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.
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影响因子：
2.5
作者：
Li, Xin;Huang, Wei;Rooney, William D.
通讯作者：
Rooney, William D.
影响因子：
10.6
作者：
Chandra, Shekhar S.;Dowling, Jason A.;Fripp, Jurgen
通讯作者：
Fripp, Jurgen
影响因子：
2.5
作者：
CLARKE, LP;VELTHUIZEN, RP;SILBIGER, M
通讯作者：
SILBIGER, M
影响因子：
5.9
作者：
Barentsz JO;Richenberg J;Clements R;Choyke P;Verma S;Villeirs G;Rouviere O;Logager V;Fütterer JJ;European Society of Urogenital Radiology
通讯作者：
European Society of Urogenital Radiology
影响因子：
4.4
作者：
Kitajima, Kazuhiro;Kaji, Yasushi;Sugimura, Kazuro
通讯作者：
Sugimura, Kazuro