Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation.
Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation.
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DOI：
10.1016/j.neuroimage.2009.02.013
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发表时间：
2009-07-01
期刊：
影响因子：
5.7
通讯作者：
Lemieux L
中科院分区：
文献类型：
--
作者：
Chupin M;Hammers A;Liu RS;Colliot O;Burdett J;Bardinet E;Duncan JS;Garnero L;Lemieux L
从MRI分割宏观上不明确和高度可变的结构，如海马（Hc）和杏仁核（Am），需要使用特定的约束。在这里，我们描述和评估一个快速的全自动混合分割，使用来自概率地图集和解剖标志的知识，从一个半自动的方法。该算法一开始就设计用于健康受试者和海马硬化患者的图像。从16名健康受试者建立概率图谱，使用SPM 5注册。自动检测和校正图谱配准步骤中的局部失配。对16名年轻受试者进行了手动分割的定量评价，采用留一策略，8名对照组和15名患有不同程度海马硬化的癫痫患者的混合队列，以及8名健康受试者在3 T扫描仪上获得。计算了7个性能指标，其中体积误差RV和Dice重叠K。该方法被证明是快速，稳健和准确的。对于Hc，新方法的结果为：16例年轻受试者{RV = 5%，K = 87%};混合队列{RV = 8%，K = 84%}; 3例T队列{RV = 9%，K = 85%}。结果优于基于图谱（阈值概率图）或半自动分割。图谱失配检测和校正被证明是有效的，最scopeHc。对于Am，结果为：16名年轻对照{RV = 7%，K = 85%};混合队列{RV = 19%，K = 78%}; 3名T队列{RV = 10%，K = 77%}。对于16名年轻受试者，结果优于半自动分割，也优于基于图谱的分割。
The segmentation from MRI of macroscopically ill-defined and highly variable structures, such as the hippocampus (Hc) and the amygdala (Am), requires the use of specific constraints. Here, we describe and evaluate a fast fully automatic hybrid segmentation that uses knowledge derived from probabilistic atlases and anatomical landmarks, adapted from a semi-automatic method. The algorithm was designed at the outset for application on images from healthy subjects and patients with hippocampal sclerosis. Probabilistic atlases were built from 16 healthy subjects, registered using SPM5. Local mismatch in the atlas registration step was automatically detected and corrected. Quantitative evaluation with respect to manual segmentations was performed on the 16 young subjects, with a leave-one-out strategy, a mixed cohort of 8 controls and 15 patients with epilepsy with variable degrees of hippocampal sclerosis, and 8 healthy subjects acquired on a 3 T scanner. Seven performance indices were computed, among which error on volumes RV and Dice overlap K. The method proved to be fast, robust and accurate. For Hc, results with the new method were: 16 young subjects {RV = 5%, K = 87%}; mixed cohort {RV = 8%, K = 84%}; 3 T cohort {RV = 9%, K = 85%}. Results were better than with atlas-based (thresholded probability map) or semi-automatic segmentations. Atlas mismatch detection and correction proved efficient for the most sclerotic Hc. For Am, results were: 16 young controls {RV = 7%, K = 85%}; mixed cohort {RV = 19%, K = 78%}; 3 T cohort {RV = 10%, K = 77%}. Results were better than with the semi-automatic segmentation, and were also better than atlas-based segmentations for the 16 young subjects.
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