Evaluation of the impact of domain adaptation on segmentation of Multiple Sclerosis lesions in MRI

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

de Sousa, IM (Autor) Outra; Oliveira, Md (Autor) Outra; Lisboa Filho, PN (Autor) Outra; Jaime S Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Houston, TX, USA, December 9-12, 2021

Páginas: 1786-1790

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00W-762

DOI: 10.1109/bibm52615.2021.9669533

Abstract (EN): Multiple Sclerosis (MS) is a chronic and inflammatory disorder that causes degeneration of axons in brain white matter and spinal cord. Magnetic Resonance Imaging (MRI) is extensively used to identify MS lesions and evaluate the progression of the disease, but the manual identification and quantification of lesions are time consuming and error-prone tasks. Thus, automated Deep Learning methods, in special Convolutional Neural Networks (CNNs), are becoming popular to segment medical images. It has been noticed that the performance of those methods tends to decrease when applied to MRI acquired under different protocols. The aim of this work is to statistically evaluate the possible influence of domain adaptation during the training process of CNNs models for segmenting MS lesions in MRI. The segmentation models were tested on MRIs (FLAIR and T1) of 20 patients diagnosed with Multiple Sclerosis. The set of segmented images of each different model was compared statistically, through the metrics Dice Similarity Coefficient (DSC), Predictive Positive Value (PPV) and Absolute Volume Difference (AVD). The results indicate that the domain adapted training can improve the performance of automatic segmentation methods, by CNNs, and have great potential to be used in medical clinics in the future. © 2021 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica