In this thesis, we investigated how in medical image segmentation, apriori information about objects’ shape knowledge can be used to guide the lungs segmentation from chest X-ray images. We propose a deep convolutional neural network for lung segmentation, where shape information is represented by a statistical representation, based on principal components analysis of lung masks computed using the training dataset and described by their Signed Distance Functions. In this regard we provide a nonlinear extension, considering the expediency of KernelPCA and the related kernel functions. As a result, the proposed network learns to predict shapes instead of learning pixel-wise classification. The segmentation method named Shape Predictor Network (SP-Net) was applied to chest X-ray images of Covid-19 positive patients where shape could be of utmost importance. Results showed that SP-Net could constrain the predicted shape to resemble a pulmonary-like structure in those cases where the pixel-wise segmentation methods failed in the presence of a significant image artifact. Finally, we compared SP-Net with state-of-the-art methods. U-net, a convolutional neural network particularly effective for medical image segmentation.