Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
This work addresses the need for real-time segmentation in fetal MRI to enable motion tracking and 3D reconstruction, representing a strong specific gain in a domain-specific application.
The paper tackled the problem of real-time automatic fetal brain extraction in fetal MRI, which is challenging due to arbitrary orientation, surrounding organs, and motion, and developed a deep learning method based on 2D U-net and autocontext that achieved average Dice metrics of 96.52% and 78.83% on normal and challenging test sets, respectively, with a test run time of about 1 second.
Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.