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Automatic detection of over 100 anatomical landmarks in medical CT images: A framework with independent detectors and combinatorial optimization

机译：在医疗CT图像中自动检测100多个解剖标记：独立探测器的框架和组合优化

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摘要

An automatic detection method for 197 anatomically defined landmarks in computed tomography (CT) volumes is presented. The proposed method can handle missed landmarks caused by detection failure, a limited imaging range and other problems using a novel combinatorial optimization framework with a two-stage sampling algorithm. After a list of candidates is generated by each landmark detector, the best combination of candidates is searched for by a combinatorial optimization algorithm using a landmark point distribution model (L-PDM) to provide prior knowledge. Optimization is performed by simulated annealing and iterative Gibbs sampling. Prior to each cycle of Gibbs sampling, another sampling algorithm is processed to estimate the spatial distribution of each target landmark, so that landmark positions without any correct detector-derived candidates can be estimated. The proposed method was evaluated using 104 CT volumes with various imaging ranges. The overall average detection distance error was 6.6 mm, and 83.8, 93.2 and 96.5% of landmarks were detected within 10, 15 and 20 mm from the ground truth, respectively. The proposed method worked even when most of the landmarks were outside of the imaging range. The identification accuracy of the vertebral centroid was also evaluated using public datasets and the proposed method could identify 70% of vertebrae including severely diseased ones. From these results, the feasibility of our framework in detecting multiple landmarks in various CT datasets was validated. (C) 2016 Elsevier B.V. All rights reserved.
机译：提出了197个解剖学定义了计算断层扫描（CT）卷中的自动检测方法。所提出的方法可以处理由检测失败，有限的成像范围和使用具有两级采样算法的新组合优化框架引起的错过的地标。在每个地标检测器生成候选者列表之后，通过使用地标点分布模型（L-PDM）来通过组合优化算法搜索最佳候选者组合以提供先验知识。通过模拟退火和迭代GIBBS采样进行优化。在GIBBS采样的每个周期之前，处理另一种采样算法以估计每个目标地标的空间分布，从而可以估计没有任何正确检测器导出的候选的地标位置。使用具有各种成像范围的104ct卷来评估所提出的方法。总平均检测距离误差为6.6毫米，83.8，93.2和96.5％的地标分别在10,15和20毫米的地面真实内检测到。即使大多数地标都在成像范围之外，所提出的方法也是如此。使用公共数据集还评估椎体质的鉴定精度，并且所提出的方法可以鉴定70％的椎骨，包括严重患病的椎骨。从这些结果，验证了我们在检测各种CT数据集中检测多个地标中的框架的可行性。 （c）2016年Elsevier B.v.保留所有权利。

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