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Spine detection in CT and MR using iterated marginal space learning

机译：使用迭代边缘空间学习的CT和MR中的脊柱检测

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摘要

Examinations of the spinal coiumn with both, Magnetic Resonance (MR) imaging and Computed Tomography (CT), often require a precise three-dimensional positioning, angulation and labeling of the spinal disks and the vertebrae. A fully automatic and robust approach is a prerequisite for an automated scan alignment as well as for the segmentation and analysis of spinal disks and vertebral bodies in Computer Aided Diagnosis (CAD) applications. In this article, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects. It is used to simultaneously detect and label the spinal disks. While a novel iterative version of MSL is used to quickly generate candidate detections comprising position, orientation, and scale of the disks with high sensitivity, the anatomical network selects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Finally, we propose an optional case-adaptive segmentation approach that allows to segment the spinal disks and vertebrae in MR and CT respectively. Since the proposed approaches are learning-based, they can be trained for MR or CT alike. Experimental results based on 42 MR and 30 CT volumes show that our system not only achieves superior accuracy but also is among the fastest systems of its kind in the literature. On the MR data set the spinal disks of a whole spine are detected in 11.5 s on average with 98.6% sensitivity and 0.073 false positive detections per volume. On the CT data a comparable sensitivity of 98.0% with 0.267 false positives is achieved. Detected disks are localized with an average position error of 2.4 mm/3.2 mm and angular error of 3.9°/4.5° in MR/CT, which is close to the employed hypothesis resolution of 2.1 mm and 3.3°.
机译：脊柱融合与磁共振（MR）成像和计算断层扫描（CT）的检查通常需要精确的三维定位，角度和标记的脊椎盘和椎骨。全自动和鲁棒的方法是自动扫描对齐的先决条件，以及对计算机辅助诊断（CAD）应用中的脊椎盘和椎体的分割和分析。在本文中，我们提出了一种新的方法，该方法结合了边缘空间学习（MSL），最近引入的概念用于有效的鉴别对象检测，具有用于检测多个对象的相对姿势信息的生成解剖网络。它用于同时检测和标记脊柱磁盘。虽然用于快速生成MSL的新颖迭代版本，用于快速生成包含具有高灵敏度的磁盘的位置，方向和比例的候选检测，但是解剖网络使用在各个九维变换空间上的所学到的学习中选择最可能的候选。最后，我们提出了一种可选的案例适应性分割方法，允许分别在MR和CT中分段脊髓囊和椎骨。由于拟议的方法是基于学习的，因此可以为MR或CT培训。基于42 MR和30 CT卷的实验结果表明，我们的系统不仅实现了卓越的准确性，而且是在文献中最快的系统。在MR数据集上，整个脊柱的脊椎盘平均检测到11.5秒，灵敏度为98.6％和0.073个假阳性检测。在CT数据上，实现了98.0％的相当灵敏度，实现了0.267个假阳性。检测到的盘是本地化的2.4 mm / 3.2 mm的平均位置误差，在MR / CT中的角度误差为3.9°/ 4.5°，接近所采用的假设分辨率为2.1 mm和3.3°。

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