Rapid and automatic localization of the anterior and posterior commissure point landmarks in MR volumetric neuroimages
Rapid and automatic localization of the anterior and posterior commissure point landmarks in MR volumetric neuroimages
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DOI：
10.1016/j.acra.2005.08.023
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发表时间：
2006-01-01
影响因子：
4.8
通讯作者：
Nowinski, WL
中科院分区：
文献类型：
--
作者：
Prakash, KNB;Hu, QM;Nowinski, WL
理据和目标。准确识别前连合(AC)和后连合(PC)在神经放射学、功能神经外科、人脑地形图和神经科学研究中是至关重要的。此外，主要的立体定向脑图谱都是基于AC和PC的。我们的目标是提供一种快速、稳健、准确和自动识别AC和PC的算法。该方法利用AC、PC和周围结构的解剖学和放射学特性，包括形态可变性。本地化分两个阶段进行：粗略和精细。粗略阶段通过分析AC和PC与胼胝体、穹隆和脑干的关系，在正中矢状面上定位AC和PC。FINE阶段以明确的感兴趣体积精炼AC和PC，分析侧脑室和第三脑室、大脑半球间裂和马萨中间区的位置。该算法使用简单的操作，如组织图、阈值、区域生长、ID投影。在94个不同的T1W和SPGR数据集上进行了测试。在精细阶段后，71个(76%)的体积误差在0-1 mm(AC)和55个(59%)的PC(59%)之间。平均误差为1.0 mm(AC)和1.0 mm(PC)。由于采用了精细的分段处理，精度提高了一倍。该算法在P4，2.5 GHz上的粗处理时间约为1s，精细处理时间约为4s。解剖学和放射学知识的使用，包括算法公式中的可变性，有助于更准确和稳健地定位结构。这种全自动算法在临床设置和研究中具有潜在的实用价值。
Rationale and Objective. Accurate identification of the anterior commissure (AC) and posterior commissure (PC) is critical in neuroradiology, functional neurosurgery, human brain mapping, and neuroscience research. Moreover, major stereotactic brain atlases are based on the AC and PC. Our goal is to provide an algorithm for a rapid, robust, accurate and automatic identification of AC and PC.Materials and Method. The method exploits anatomical and radiological properties of AC, PC and surrounding structures, including morphological variability. The localization is done in two stages: coarse and fine. The coarse stage locates the AC and PC on the midsagittal plane by analyzing their relationships with the corpus callosum, fornix, and brainstem. The fine stage refines the AC and PC in a well-defined volume of interest, analyzing locations of lateral and third ventricles, interhemispheric fissure, and massa intermedia.Results. The algorithm was developed using simple operations, like histogramming, thresholding, region growing, ID projections. It was tested on 94 diversified T1W and SPGR datasets. After the fine stage, 71 (76%) volumes had an error between 0-1 mm for the AC and 55 (59%) for the PC. The mean errors were 1.0 mm (AC) and 1.0 mm (PC). The accuracy has improved twice due to fine stage processing. The algorithm took about I second for coarse and 4 seconds for fine processing on P4, 2.5 GHz.Conclusion. The use of anatomical and radiological knowledge including variability in algorithm formulation aids in localization of structures more accurately and robustly. This fully automatic algorithm is potentially useful in clinical setting and for research.