ИСТИНА

Diffuse axonal injury (DAI) is a common type of brain damage induced by trauma. Accurate detection and quantification of DAI lesions is essential for assessment of a patient’s state and making a prognosis of a traumatic disease. This study is devoted to semi-automated segmentation of DAI lesions on T2*-weighted brain magnetic resonance (MR) images. A radiologist outlines the regions containing lesions, and the lesions are further automatically delineated inside these regions. A proposed algorithm for automated segmentation of DAI lesions inside specified regions is based on contouring algorithm which treats the image as a 3D topological map where a pixel’s intensity corresponds to its height. It builds isolines of an intensity function. Lesion contours are among these isolines. In order to distinguish true lesion contours from other closed contours obtained by contouring algorithm machine learning approach is exploited. A labeled training base with positive (lesions) and negative (non-lesions or lesion parts) examples of closed contours is used to train the classifier. The algorithm was evaluated with real T2*-weighted brain MRI images.