Velasco-Annis Clemente, Akhondi-Asl Alireza, Stamm Aymeric, Warfield Simon K
Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, MA.
J Neuroimaging. 2018 Mar;28(2):162-172. doi: 10.1111/jon.12483. Epub 2017 Nov 14.
Segmentation of human brain structures is crucial for the volumetric quantification of brain disease. Advances in algorithmic approaches have led to automated techniques that save time compared to interactive methods. Recently, the utility and accuracy of template library fusion algorithms, such as Local MAP PSTAPLE (PSTAPLE), have been demonstrated but there is little guidance regarding its reproducibility compared to single template-based algorithms such as FreeSurfer and FSL-FIRST.
Eight repeated magnetic resonance imagings of 20 subjects were segmented using FreeSurfer, FSL-FIRST, and PSTAPLE. We reported the reproducibility of segmentation-derived volume measurements for brain structures and calculated sample size estimates for detecting hypothetical rates of tissue atrophy given the observed variances.
PSTAPLE had the most reproducible volume measurements for hippocampus, putamen, thalamus, caudate, pallidum, amygdala, Accumbens area, and cortical regions. FreeSurfer was most reproducible for brainstem. PSTAPLE was the most accurate algorithm in terms of several metrics include Dice's coefficient. The sample size estimates showed that a study utilizing PSTAPLE would require tens to hundreds less subjects than the other algorithms for detecting atrophy rates typically observed in brain disease.
PSTAPLE is a useful tool for automatic human brain segmentation due to its precision and accuracy, which enable the detection of the size of the effect typically reported for neurological disorders with a substantially reduced sample size, in comparison to the other tools we assessed. This enables randomized controlled trials to be executed with reduced cost and duration, in turn, facilitating the assessment of new therapeutic interventions.
人脑结构的分割对于脑部疾病的体积量化至关重要。算法方法的进步催生了自动化技术，与交互式方法相比，节省了时间。最近，模板库融合算法（如局部MAP PSTAPLE，简称PSTAPLE）的实用性和准确性已得到证明，但与基于单模板的算法（如FreeSurfer和FSL-FIRST）相比，关于其可重复性的指导却很少。
使用FreeSurfer、FSL-FIRST和PSTAPLE对20名受试者的8次重复磁共振成像进行分割。我们报告了脑结构分割衍生体积测量的可重复性，并根据观察到的方差计算了检测假设组织萎缩率所需的样本量估计值。
对于海马体、壳核、丘脑、尾状核、苍白球、杏仁核、伏隔核区域和皮质区域，PSTAPLE的体积测量具有最高的可重复性。FreeSurfer对脑干的分割最具可重复性。就包括Dice系数在内的几个指标而言，PSTAPLE是最准确的算法。样本量估计表明，与其他算法相比，使用PSTAPLE的研究检测脑部疾病中通常观察到的萎缩率所需的受试者数量要少几十到几百人。
PSTAPLE因其精度和准确性，是自动人脑分割的有用工具，与我们评估的其他工具相比，它能够以显著减少的样本量检测出通常报道的神经系统疾病的效应大小。这使得随机对照试验能够以更低的成本和更短的时间进行，进而有助于评估新的治疗干预措施。
