Authors:
Giuseppe Placidi
1
;
Luigi Cinque
2
and
Matteo Polsinelli
1
Affiliations:
1
A2VI-Lab, c/o Department of Life, Health and Environmental Sciences, University of L’Aquila, Coppito 2 AQ, 67100, Italy
;
2
Department of Computer Science, Sapienza University of Rome, Via Salaria 113 RM, 00198, Italy
Keyword(s):
Image Identification, Image Segmentation, Multiple Sclerosis, MRI, Convolutional Neural Networks.
Abstract:
General constraints for automatic identification/segmentation of multiple sclerosis (MS) lesions by Magnetic Resonance Imaging (MRI) are discussed and guidelines for effective training of a supervised technique are presented. In particular, system generalizability to different imaging sequences and scanners from different manufacturers, misalignment between images from different modalities and subjectivity in generating labelled images, are indicated as the main limitations to high accuracy automatic MS lesions identification/segmentation. A convolutional neural network (CNN) based method is used by applying the suggested guidelines and preliminary results demonstrate the improvements. The method has been trained, validated and tested on publicly available labelled MRI datasets. Future developments and perspectives are also presented.
