Empirical Comparison of Deep Neural Networks for Brain Vessel Segmentation

Abstract

Examination, monitoring and analysis of structural changes in the blood vessels of the brain enable the observation of brain functions. Therefore, the segmentation of the entire cerebral vascular network (including the capillaries) is of great importance in terms of the relevant specialist’s opinion on the diagnosis and treatment of a disease. When performed manuall, segmentation of the vascular network of the brain is a long time-consuming and fault-tolerant process. The automatic segmentation of the brain microvascular structure with machine learning approaches eliminates the need for specialists, and provides a method for perfroming cerebral vessel segmentation in a short time. This study provides the empirical comparision of three different deep neural network models including autoencoder, U-Net and ResNet+U-Net for the vascular network segmentation of brain vessels. The experiments are conducted on vesseINN dataset, which is a volumetric cerebrovascular system dataset obtained by two-photon microscopy. The models are evaluated based on accuracy, f1-score, recall, and precision metrics. During the training phase, U-Net and ResNet+Unet achieve 98% accuracy. Auto-encoder, on the other hand, yields 95% accuracy. In the test phase, it is observed that U-Net and ResNet+U-Net models give better results than the autoencoder model, according to the results obtained with 97% accuracy for U-Net and ResNet+Unet networks and 95% accuracy for autoencoder.