Deparment of Medical Imaging, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Toronto, ON, M4N 3M5, Canada.
Ecole de Technologie Superieure (ETS), 1100 Rue Notre-Dame O, Montreal, QC, H3C 1K3, Canada.
Int J Comput Assist Radiol Surg. 2017 Nov;12(11):1911-1922. doi: 10.1007/s11548-017-1651-0. Epub 2017 Aug 7.
This study investigates an efficient (nearly real-time) two-stage spine labeling algorithm that removes the need for an external training while being applicable to different types of MRI data and acquisition protocols.
Based solely on the image being labeled (i.e., we do not use training data), the first stage aims at detecting potential vertebra candidates following the optimization of a functional containing two terms: (i) a distribution-matching term that encodes contextual information about the vertebrae via a density model learned from a very simple user input, which amounts to a point (mouse click) on a predefined vertebra; and (ii) a regularization constraint, which penalizes isolated candidates in the solution. The second stage removes false positives and identifies all vertebrae and discs by optimizing a geometric constraint, which embeds generic anatomical information on the interconnections between neighboring structures. Based on generic knowledge, our geometric constraint does not require external training.
We performed quantitative evaluations of the algorithm over a data set of 90 mid-sagittal MRI images of the lumbar spine acquired from 45 different subjects. To assess the flexibility of the algorithm, we used both T1- and T2-weighted images for each subject. A total of 990 structures were automatically detected/labeled and compared to ground-truth annotations by an expert. On the T2-weighted data, we obtained an accuracy of 91.6% for the vertebrae and 89.2% for the discs. On the T1-weighted data, we obtained an accuracy of 90.7% for the vertebrae and 88.1% for the discs.
Our algorithm removes the need for external training while being applicable to different types of MRI data and acquisition protocols. Based on the current testing data, a subject-specific model density and generic anatomical information, our method can achieve competitive performances when applied to T1- and T2-weighted MRI images.
本研究提出了一种高效（近乎实时）的两阶段脊柱标注算法，该算法无需外部训练，可适用于不同类型的 MRI 数据和采集协议。
仅基于待标注的图像（即，我们不使用训练数据），第一阶段旨在通过优化包含两项内容的函数来检测潜在的候选椎体：（i）分布匹配项，通过从非常简单的用户输入中学习的密度模型来对椎体的上下文信息进行编码，这相当于在预定义的椎体上点击鼠标；（ii）正则化约束项，它会惩罚解中的孤立候选体。第二阶段通过优化几何约束来去除假阳性并识别所有的椎体和椎间盘，该约束嵌入了关于相邻结构之间连接的通用解剖学信息。基于通用知识，我们的几何约束不需要外部训练。
我们在 45 个不同个体的 90 个腰椎中矢状面 MRI 图像数据集上对算法进行了定量评估。为了评估算法的灵活性，我们对每个个体使用了 T1 加权和 T2 加权图像。总共自动检测/标注了 990 个结构，并由专家与真实标注进行了比较。在 T2 加权数据上，椎体的准确率为 91.6%，椎间盘的准确率为 89.2%。在 T1 加权数据上，椎体的准确率为 90.7%，椎间盘的准确率为 88.1%。
我们的算法无需外部训练，可适用于不同类型的 MRI 数据和采集协议。基于当前的测试数据，使用基于个体的模型密度和通用解剖学信息，我们的方法在应用于 T1 和 T2 加权 MRI 图像时可以达到有竞争力的性能。
