Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
复制标题
利用骨关节炎倡议的数据进行膝关节图像量化，自动分割高场和低场膝关节 MRI 图像
DOI：
10.1117/1.jmi.2.2.024001
复制
发表时间：
2015-04-01
影响因子：
2.4
通讯作者：
Nielsen, Mads
中科院分区：
文献类型：
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作者：
Dam, Erik B.;Lillholm, Martin;Nielsen, Mads
临床研究，包括数以千计的磁共振成像（MRI）扫描提供了潜在的发病机制研究骨关节炎。然而，全面量化所有骨、软骨和半月板间室是具有挑战性的。我们提出了一个用于膝关节MRI全自动分割的分割框架。该框架将多图集刚性配准与体素分类相结合，并在具有不同骨骼、软骨和骨水泥配置的手动分割上进行训练。验证包括来自临床和基础研究中心的高场和低场膝关节MRI队列、骨关节炎倡议（QAI）和膝关节图像分割10（SKI 10）挑战。在评价过程中，共对1907个膝关节MRI进行了分段。未排除分割。我们得到的OAI软骨体积分数可应要求提供。精确度和准确度性能与手动读取器重新分割匹配良好。软骨体积扫描-再扫描精度为4.9%（RMS CV）。内侧/外侧胫骨/股骨软骨间室中的Dice体积重叠为0.80至0.87。在OAI扫描上，独立方法与体积的相关性在0.90和0.96之间。因此，该框架证明了与手动分割相当的精确度和准确度。最后，我们的方法在SKI 10挑战中在软骨分割方面排名第二。综合验证表明，自动分割适用于具有数千次扫描的队列。(C)作者。由SPIE在知识共享署名3.0未移植许可下发布。分发或复制本作品的全部或部分要求完全归属于原始出版物，包括其DOI。
Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high-and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.