MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

中文翻译：

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形状约束的可变形大脑分割：方法和定量验证


MRI 引导的神经干预需要对大脑解剖结构进行快速、准确和可重复的分割，以便在手术过程中识别目标并在术后评估干预效率。分割算法必须经过验证并批准用于临床使用。这项工作介绍了一种形状受限的可变形大脑分割方法，描述了用于其临床清除的定量验证，并与手动专家分割和 FreeSurfer（一种用于神经影像数据分析的开源软件）进行了比较。 ClearPoint Maestro 是一款用于根据 T1 加权 MRI 进行全自动大脑分割的软件，它将形状受限的可变形大脑模型与大脑半球和小脑内的体素组织分割相结合。分割的性能在准确性和再现性方面得到了验证。根据训练数据和独立追踪的地面实况评估分割准确性。分割的再现性进行了量化，并与手动专家分割和 FreeSurfer 进行了比较。定量再现性分析表明，与手动专家分割和 FreeSurfer 相比，性能更优越。形状受限的方法可以实现准确且高度可重复的分割。基于固有点的对应性提供了一致的目标识别，非常适合 MRI 引导的神经干预。