Automatic linear measurements of the fetal brain on MRI with deep neural networks

PURPOSE

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 for fetal 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.

METHODS

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.

RESULTS

Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean [Formula: see text] difference of 1.55 mm, 1.45 mm and 1.23 mm, respectively, and a Bland-Altman 95% confidence interval ([Formula: see text] of 3.92 mm, 3.98 mm and 2.25 mm, respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.

CONCLUSIONS

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.

Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning

Measurement of the width of fetal lateral ventricles (LVs) in prenatal ultrasound (US) images is essential for antenatal neuronographic assessment. However, the manual measurement of LV width is highly subjective and relies on the clinical experience of scanners. To deal with this challenge, we propose a computer-aided detection framework for automatic measurement of fetal LVs in two-dimensional US images. First, we train a deep convolutional network on 2,400 images of LVs to perform pixel-wise segmentation. Then, the number of pixels per centimeter (PPC), a vital parameter for quantifying the caliper in US images, is obtained via morphological operations guided by prior knowledge. The estimated PPC, upon conversion to a physical length, is used to determine the diameter of the LV by employing the minimum enclosing rectangle method. Extensive experiments on a self-collected dataset demonstrate that the proposed method achieves superior performance over manual measurement, with a mean absolute measurement error of 1.8 mm. The proposed method is fully automatic and is shown to be capable of reducing measurement bias caused by improper US scanning.