Science-Based Platforms, Fort Pierce, Florida, United States of America.
GYM Group SA, Cali, Colombia.
PLoS One. 2023 May 11;18(5):e0285414. doi: 10.1371/journal.pone.0285414. eCollection 2023.
Manual segmentation, which is tedious, time-consuming, and operator-dependent, is currently used as the gold standard to validate automatic and semiautomatic methods that quantify geometries from 2D and 3D MR images. This study examines the accuracy of manual segmentation and generalizes a strategy to eliminate its use. Trained individuals manually measured MR lateral ventricles images of normal and hydrocephalus infants from 1 month to 9.5 years of age. We created 3D-printed models of the lateral ventricles from the MRI studies and accurately estimated their volume by water displacement. MRI phantoms were made from the 3D models and images obtained. Using a previously developed artificial intelligence (AI) algorithm that employs four features extracted from the images, we estimated the ventricular volume of the phantom images. The algorithm was certified when discrepancies between the volumes-gold standards-yielded by the water displacement device and those measured by the automation were smaller than 2%. Then, we compared volumes after manual segmentation with those obtained with the certified automation. As determined by manual segmentation, lateral ventricular volume yielded an inter and intra-operator variation up to 50% and 48%, respectively, while manually segmenting saggital images generated errors up to 71%. These errors were determined by direct comparisons with the volumes yielded by the certified automation. The errors induced by manual segmentation are large enough to adversely affect decisions that may lead to less-than-optimal treatment; therefore, we suggest avoiding manual segmentation whenever possible.
手动分割，既繁琐又耗时，且依赖于操作人员，目前被用作验证自动和半自动方法从二维和三维磁共振图像量化几何形状的金标准。本研究检查了手动分割的准确性，并推广了一种消除其使用的策略。受过训练的人员手动测量了正常和脑积水婴儿从 1 个月到 9.5 岁的磁共振侧脑室图像。我们根据 MRI 研究创建了侧脑室的 3D 打印模型，并通过水置换准确估计了它们的体积。从 3D 模型和获得的图像中制作了 MRI 模型。使用先前开发的人工智能（AI）算法，该算法采用从图像中提取的四个特征，我们估计了幻影图像的心室容积。当水置换装置产生的体积与自动化测量的体积之间的差异小于 2%时，该算法就得到了认证。然后，我们比较了手动分割后的体积与经认证的自动化测量的体积。通过手动分割确定的侧脑室体积，分别具有高达 50%和 48%的内外操作员变化，而手动分割矢状图像会产生高达 71%的误差。这些误差是通过与经认证的自动化产生的体积进行直接比较来确定的。手动分割引起的误差足以对可能导致治疗效果不佳的决策产生不利影响；因此，我们建议尽可能避免手动分割。
