Automatic Segmentation of Hemorrhagic Stroke on Brain CT Images Using Convolutional Neural Networks Through Fine-Tuning

Strokes are among the top three global causes of death. The diagnosis of stroke is commonly made based on the symptoms displayed and, specifically, on the the results of the neuroimaging tests. Currently, computed tomography (CT) is the fastest, most accessible, and most financially viable neuroimaging method. Thus, Computer-Aided Diagnosis (CAD) systems that can analyze the CT images are essential for obtaining diagnostic information. In order to aid medical diagnosis, this paper proposes a new automatic method of segmenting the areas affected by a hemorrhagic stroke in the CT images based on deep learning, using Mask R-CNN combined with Windowing of Parzen, Clustering and Region Growing methods through the techniques of fine-tuning. Our best model achieved an accuracy of 99.72% and the segmentation time of 6.49 s. Thus, we surpass methods already consolidated in the literature, with both manual and automatic initialization, and even Deep Learning techniques using fine-tuning. We validate the proposed model by comparing it to existing methods that segment images from the same dataset. We overcoming the state of art among the analyzed models, which proves the efficiency of our method for systems based on CAD.