Optic pathway gliomas (OPG) are heterogeneous tumors with complex shape. The aim of this study was to implement a deep-learning approach for automatic segmentation and follow-up of patients with OPG based on conventional MRI. A total of 354 MRI scans from 53 patients where included. A neural-network with a U-net architecture was trained for segmentation of lesion area. The similarity coefficient score between segmentation results and ground truth was 0.812±0.159, with sensitivity=0.799±0.188, specificity=0.999±0.002 and correlation of r=0.987 (p<0.001) between lesion volumes. These results demonstrate the potential applicability of the proposed method for automatic radiological follow-up of patients with OPG.