Recent research has determined links between the development of some neurodevelopmental disorders, including Autism Spectrum Disorder (ASD), and the morphometry of subcortical brain structures in children aged 12 and 24 months old, which is usually obtained via segmentation of a Magnetic Resonance (MR) image. Deep-learning based methods, such as the u-net, have emerged as the fastest way to compute segmentations with admissible accuracy. I have constructed a segmentation pipeline with a processing step and neural network step using a 3D u-net model trained on processed data from 830 automatically segmented images and 27 manually segmented images. The model is trained with a loss function that has structure volume and surface accuracy considerations. The segmentation pipeline is available as an open source software on GitHub. The model is validated on a set of 5 manually segmented images and obtains moderately good results compared to the state-of-the-art.Bachelor of Scienc
