Image Enhancement and Segmentation of Magnetic Resonance Cerebral Vessels Through Conventional and Deep Learning Techniques

The study of brain vascular patterns in preterm infants is relevant for identifying pathologies associated with brain irrigation. However, several drawbacks arise while using these types of images for diagnosis, such as noisy images and difficulties in the quantification of the vessel patterns. The goal of this research is to enhance the images for a subsequent segmentation stage. Thus, as a result of this research, an entire pipeline of denoising and segmentation is presented as a solution. For denoising the images, the combination of conventional techniques with unsupervised techniques based on deep learning was explored. The best method for the removal of noise was the combination of traditional methods and PN2V using a GMM model. A UNet model was trained utilizing noisy pictures for segmentation. Then it was tested using both denoised and noisy images. The findings demonstrated an improvement of 9.4% in the dice score when the model was trained using noisy images.