Prados Ferran, Ashburner John, Blaiotta Claudia, Brosch Tom, Carballido-Gamio Julio, Cardoso Manuel Jorge, Conrad Benjamin N, Datta Esha, Dávid Gergely, Leener Benjamin De, Dupont Sara M, Freund Patrick, Wheeler-Kingshott Claudia A M Gandini, Grussu Francesco, Henry Roland, Landman Bennett A, Ljungberg Emil, Lyttle Bailey, Ourselin Sebastien, Papinutto Nico, Saporito Salvatore, Schlaeger Regina, Smith Seth A, Summers Paul, Tam Roger, Yiannakas Marios C, Zhu Alyssa, Cohen-Adad Julien
Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK.
Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
Neuroimage. 2017 May 15;152:312-329. doi: 10.1016/j.neuroimage.2017.03.010. Epub 2017 Mar 7.
An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.
脊髓磁共振成像中的一个重要图像处理步骤是能够可靠且准确地分割灰质和白质，以进行组织特异性分析。有几种半自动或全自动分割方法用于测量颈髓横截面积，其性能优异，接近或等同于手动分割。然而，由于灰质的横截面尺寸小且形状不规则，灰质分割仍然具有挑战性，世界各地的多个研究小组正在该领域开展积极研究。因此，组织了一项灰质脊髓分割挑战赛，以使用通过不同的3D梯度回波序列采集的同一多中心、多供应商数据集来测试各种方法的不同能力。这项挑战赛旨在表征该领域的当前技术水平，并识别未来改进的新机会。将世界各地不同研究小组独立开发的六种不同的脊髓灰质分割方法及其性能与手动分割结果（当前的金标准）进行了比较。所有算法在检测灰质蝴蝶方面都提供了良好的总体结果，尽管在某些分割质量指标上性能有所不同。数据已公开提供，挑战赛网站仍接受新的提交。出于本出版物的目的，本次挑战赛未对任何所展示的方法进行修改。
