GEMO: A Deep Learning Method for Brain Fiber Classification and Tract Segmentation Using Geometrical and Morphological Features.

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Abstract

Diffusion-weighted magnetic resonance imaging (dMRI) is often used to study brain structure. One of the important applications made possible by dMRI is streamline tractography that is utilized for structural connectivity evaluation and neurosurgical planning. Reliable localization of brain fibers can considerably enhance the outcome of these applications. In this study, a supervised deep learning approach entitled "GEMO" was proposed for fiber classification. This approach benefits from GEometrical and MOrphological features in addition to the features extracted from a convolutional neural network for improving the final classification performance. Streamlines are transformed from three-dimensional coordinate space into two-dimensional color-encoded images using the "xyz2RGB" mapping method and are subsequently fed into a convolutional neural network for learning and classification. Our results demonstrate that incorporating morphological and geometric features of streamlines enhances the accuracy of the deep learning network in the classification of fibers in comparison with the case where no extracted features were employed. An individual analysis of these features further revealed that they can increase the network's accuracy to as high as 98.34%. GEMO demonstrated consistently higher performance across six segmentation accuracy metrics compared with other existing approaches. Our method performs the streamline classification from a whole brain tractogram, only focusing on each streamline's features, which include those provided from CNN in addition to geometric and morphologic features. GEMO does not need any structural images or preprocessing and outperforms the state-of-the-art segmentation methods. Moreover, it can facilitate further assessment of brain structures by providing automated white matter tract segmentations.

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