An automatic framework for multiple sclerosis (MS) follow-up by Magnetic Resonance Imaging (MRI) is presented. It is based on the identification and segmentation of lesions by using convolutional neural network (CNN) architecture applied to the volumes collected by different imaging modalities and on the registration of the volumes obtained by two consecutive examinations. The resulting binary masks obtained from the identification/segmentation strategy on each examination are used to calculate the volume of each lesions, their status (chronic or active) and, hence, to estimate the progression of the disease. Preliminary results are reported demonstrating that the calculations performed by the proposed framework are capable, when the disease is stable, to gather the same information obtainable when the contrast agent (CA) is administered to the patient.

Automatic framework for multiple sclerosis follow-up by magnetic resonance imaging for reducing contrast agents / Placidi, G.; Cinque, L.; Polsinelli, M.; Splendiani, A.; Tommasino, E.. - 11752:(2019), pp. 367-378. (Intervento presentato al convegno 20th International Conference on Image Analysis and Processing, ICIAP 2019 tenutosi a Trento, Italy) [10.1007/978-3-030-30645-8_34].

Automatic framework for multiple sclerosis follow-up by magnetic resonance imaging for reducing contrast agents

Cinque L.;Polsinelli M.;
2019

Abstract

An automatic framework for multiple sclerosis (MS) follow-up by Magnetic Resonance Imaging (MRI) is presented. It is based on the identification and segmentation of lesions by using convolutional neural network (CNN) architecture applied to the volumes collected by different imaging modalities and on the registration of the volumes obtained by two consecutive examinations. The resulting binary masks obtained from the identification/segmentation strategy on each examination are used to calculate the volume of each lesions, their status (chronic or active) and, hence, to estimate the progression of the disease. Preliminary results are reported demonstrating that the calculations performed by the proposed framework are capable, when the disease is stable, to gather the same information obtainable when the contrast agent (CA) is administered to the patient.
2019
20th International Conference on Image Analysis and Processing, ICIAP 2019
Convolutional neural networks; Deep learning; Image registration; Image segmentation; MRI; Multiple sclerosis
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Automatic framework for multiple sclerosis follow-up by magnetic resonance imaging for reducing contrast agents / Placidi, G.; Cinque, L.; Polsinelli, M.; Splendiani, A.; Tommasino, E.. - 11752:(2019), pp. 367-378. (Intervento presentato al convegno 20th International Conference on Image Analysis and Processing, ICIAP 2019 tenutosi a Trento, Italy) [10.1007/978-3-030-30645-8_34].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1630381
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