Abstract
Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used forfetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are cerebral biparietal diameter (CBD), bone biparietal diameter (BBD), and trans-cerebellum diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: (1) computation of a region of interest that includes the fetal brain with an anisotropic 3D U-Net classifier; (2) reference slice selection with a convolutional neural network; (3) slice-wise fetal brain structures segmentation with a multi-class U-Net classifier; (4) computation of the fetal brain midsagittal line and fetal brain orientation, and; (5) computation of the measurements. Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean [Formula: see text] difference of 1.55mm, 1.45mm and 1.23mm, respectively, and a Bland-Altman 95% confidence interval ([Formula: see text] of 3.92mm, 3.98mm and 2.25mm, respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions. The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human-level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.
Highlights
Human fetal brain development is a complex process that involves significant changes in volume, structure, and maturation in a unique spatio-temporal pattern
Experimental results on 214 volumes for Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD) and Trans-Cerebellum Diameter (TCD) measurements yielded a mean L1 difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (CI95) of 3.92mm, 3.98mm and 2.25mm respectively
The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice
Summary
Human fetal brain development is a complex process that involves significant changes in volume, structure, and maturation in a unique spatio-temporal pattern. Abnormal fetal brain development can have significant short and long-term consequences on the newborn. Accurate quantitative assessment of fetal brain growth is essential for early diagnosis of developmental disorders. Ultrasound (US) is currently the primary imaging modality to monitor fetal development. Magnetic Resonance Imaging (MRI) is increasingly used for fetal brain assessment in cases of inconclusive US findings, to confirm or reject suspected abnormalities, and to detect other developmental abnormalities. MRI-based routine clinical assessment of fetal brain development is mainly subjective, with a few biometric linear measurements. Similar to US-based evaluation, these measurements are compared to MRI reference of growth centiles of normal developing fetuses [1,2]
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