Authors:
S. Ostellino
;
A. Benso
and
G. Politano
Affiliation:
Politecnico di Torino, Computer Science and Automation Department, Torino, Italy
Keyword(s):
Multiple Sclerosis, MRI, Imaging, Pre-processing, Deep Learning, Data Preparation, Heterogeneous Data-sets, Real Clinical Data.
Abstract:
Automatic segmentation of tissues and lesions is a very important step in any Artificial Intelligence pipeline designed to analyze medical images (especially MRI). This is particularly true for brain MRI images of patients affected by neurological pathologies like Multiple Sclerosis (MS). To perform well, cutting edge Artificial Intelligence approaches like Deep Learning need a huge amount of training data. Unfortunately, available data-sets of MRI medical images often lack annotations, standardized acquisition protocols, formats and dimensions. This heterogeneity in the data-sets makes it often very difficult to use and integrate different data-sets in the same pipeline. Available image pre-processing tools have specific requirements and might not be adequate for extensive usage with heterogeneous data-sets. This paper presents an on-going work on a comprehensive and consistent brain MRI images pre-processing pipeline for Deep Learning applications enabling the creation of a congruo
us data-set. The pipeline was tested with the public available ISBI2015 data-set.
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