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 L1 difference of 1.55 mm, 1.45 mm and 1.23 mm, respectively, and a Bland–Altman 95% confidence interval (CI95) 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.
|Original language||American English|
|Number of pages||12|
|Journal||International journal of computer assisted radiology and surgery|
|State||Published - Sep 2021|
Bibliographical noteFunding Information:
This research was supported by Kamin grant 63418 from the Israel Innovation Authority; Joseph Bar Nathan Trustee of Mychor Trust Fund. We are grateful to Vicki Myers for editorial assistance, and MRI technicians for scanning the fetuses.
© 2021, CARS.
- Deep learning
- Fetal brain MRI analysis
- Fetal brain development
- Fetal brain linear measurements