In this study we propose to improve an existing artificial neural network architecture, the MPU-net, which is designed for having very few parameters for multiple sclerosis lesion segmentation on magnetic resonance images. With this improved architecture we conducted a study to assess the influence of the number of training examples on the model performance and generalization. The question behind this study is: "With an appropriate architecture, how many patients do we need?". We evaluated 9 different adaptations of the MPU-net architecture. Then, after the selection of the best architecture we learned the model multiple times with different numbers of patients and assessed its performances. The addition of deep supervision, the reduction of number of convolutional layers and the addition of regularization layers produced a more stable and performant architecture. Learnings of selected model with only 10 exams delivered performances equivalent to learnings with 23 exams. So, in our experimental setup, it is possible to learn a performant model with only 10 fully annotated examples.