Assessment of mesial temporal sclerosis through MRI processing

Abstract

Mesial temporal sclerosis (MTS) is the principal cause of complex epilepsy, is manifested principally by gliosis and hippocampal volume loss. This project aims to develop an algorithm that allows automatic measurement of hippocampal volume and signal intensity in magnetic resonance imaging. The algorithm developed uses preprocessing of the images to reduce the artifacts and for the extraction of the features were used techniques of machine learning (support vector machine) and texture analysis. Results can help to optimize time in the assessment of the mesial temporal sclerosis and can contribute to the best training to the youngers neuroradiologists.

MTS image analyzer: a software tool to identify mesial temporal sclerosis in MRI images

“…With the automatic detection of the hippocampus in mind, an efficient algorithm has been developed for detection and identification of features of MTS 13 . Figure 1 summarizes the methodology of this process.…”

“…For this reason an experienced radiologist is needed to accurately discern the changes of the hippocampal structures and abnormal signals. 7,13,14 In this context, artificial intelligence, namely Machine Learning/Deep Learning could help analyze and diagnose the MR brain images [15][16][17][18][19] as well as other parts of the human body. There is a need to detect and classify MTS images and the development of systems that could assess, detect and identify the changes of the hippocampal structures like the volume and intensity of signals.…”

“…There is a need to detect and classify MTS images and the development of systems that could assess, detect and identify the changes of the hippocampal structures like the volume and intensity of signals. 9,11,13,14,20 There is some literature 10,19,21,22 dealing with the automatic detection of the hippocampus. These systems employ the fact that there is hippocampal volume loss which is a principal feature of MTS 14 .…”