Resumen

REYES, Alejandro et al. Probabilistic Multiple Sclerosis Lesion Detection using Superpixels and Markov Random Fields. Rev. mex. ing. bioméd [online]. 2020, vol.41, n.3, e1050.  Epub 27-Ene-2021. ISSN 2395-9126.  https://doi.org/10.17488/rmib.41.3.3.

Multiple Sclerosis (MS) is the most common neurodegenerative disease among young adults. Diagnosis and monitoring of MS is performed with T2-weighted or T2 FLAIR magnetic resonance imaging, where MS lesions appear as hyperintense spots in the white matter. In recent years, multiple algorithms have been proposed to detect these lesions with varying success rates, which greatly depend on the amount of a priori information required by each algorithm, such as the use of an atlas or the involvement of an expert to guide the segmentation process. In this work, a fully automatic method that does not rely on a priori anatomical information is proposed and evaluated. The proposed algorithm is based on an over-segmentation in superpixels and their classification by means of Gauss-Markov Measure Fields (GMMF). The main advantage of the over-segmentation is that it preserves the borders between tissues, while the GMMF classifier is robust to noise and computationally efficient. The proposed segmentation is then applied in two stages: first to segment the brain region and then to detect hyperintense spots within the brain. The proposed method is evaluated with synthetic images from BrainWeb, as well as real images from MS patients. The proposed method produces competitive results with respect to other algorithms in the state of the art, without requiring user assistance nor anatomical prior information.

Palabras llave : Multiple Sclerosis; Lesion Detection; Superpixels; GMMF, Image Segmentation.