An automatic approach to fetal magnetic resonance image segmentation using 2D U-Net Architecture

<p>Fetal magnetic resonance imaging is imperative to diagnosing and treating fetal disorders because it is the most effective imaging modality given its high spatial and coarse resolutions and soft-tissue contrast. Segmentation is a required step performed by radiologists to aid clinicians to treat and track disease in utero. Segmentation is followed by biometric calculations to determine the weight of the fetus for diagnosing intrauterine growth restrictions, fetal brain and cardiac abnormalities, and other fetal and congenital disorders. However, manual segmentations are time-consuming, inaccurate, and dependent on the skills of the operator. An automatic segmentation method can mitigate these drawbacks and improve maternal-fetal health by reducing wait times for treatment and improving the accuracy and standardization of segmentations. This thesis presents an automatic algorithm using the successful deep learning model U-Net, to segment the whole fetus from and MRI of the maternal abdomen with 86.70% Dice Coefficient accuracy. This is the first convolutional neural network applied for this task and outperforms other models used for similar tasks. This novel algorithm can be applied in both clinical and research settings as pre-processing pipelines to segment the whole fetus from maternal MR images.</p>