Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.

Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from routine clinical MRI of MS patients. The proposed met...

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Autores principales:	Sibaji Gaj, Daniel Ontaneda, Kunio Nakamura
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e8588fd874664c289c25174ec0b07f61
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spelling	oai:doaj.org-article:e8588fd874664c289c25174ec0b07f612021-12-02T20:08:45ZAutomatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.1932-620310.1371/journal.pone.0255939https://doaj.org/article/e8588fd874664c289c25174ec0b07f612021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255939https://doaj.org/toc/1932-6203Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from routine clinical MRI of MS patients. The proposed method first segments the potential lesions using 2D-UNet from multi-channel scans (T1 post-contrast, T1 pre-contrast, FLAIR, T2, and proton-density) and classifies the lesions using a random forest classifier. The algorithm was trained and validated on 600 MRIs with manual segmentation. We compared the effect of loss functions (Dice, cross entropy, and bootstrapping cross entropy) and number of input contrasts. We compared the lesion counts with those by radiologists using 2,846 images. Dice, lesion-wise sensitivity, and false discovery rate with full 5 contrasts were 0.698, 0.844, and 0.307, which improved to 0.767, 0.969, and 0.00 in large lesions (>100 voxels). The model using bootstrapping loss function provided a statistically significant increase of 7.1% in sensitivity and of 2.3% in Dice compared with the model using cross entropy loss. T1 post/pre-contrast and FLAIR were the most important contrasts. For large lesions, the 2D-UNet model trained using T1 pre-contrast, FLAIR, T2, PD had a lesion-wise sensitivity of 0.688 and false discovery rate 0.083, even without T1 post-contrast. For counting lesions in 2846 routine MRI images, the model with 2D-UNet and random forest, which was trained with bootstrapping cross entropy, achieved accuracy of 87.7% using T1 pre-contrast, T1 post-contrast, and FLAIR when lesion counts were categorized as 0, 1, and 2 or more. The model performs well in routine non-standardized MRI datasets, allows large-scale analysis of clinical datasets, and may have clinical applications.Sibaji GajDaniel OntanedaKunio NakamuraPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0255939 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sibaji Gaj Daniel Ontaneda Kunio Nakamura Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
description	Gadolinium-enhancing lesions reflect active disease and are critical for in-patient monitoring in multiple sclerosis (MS). In this work, we have developed the first fully automated method to segment and count the gadolinium-enhancing lesions from routine clinical MRI of MS patients. The proposed method first segments the potential lesions using 2D-UNet from multi-channel scans (T1 post-contrast, T1 pre-contrast, FLAIR, T2, and proton-density) and classifies the lesions using a random forest classifier. The algorithm was trained and validated on 600 MRIs with manual segmentation. We compared the effect of loss functions (Dice, cross entropy, and bootstrapping cross entropy) and number of input contrasts. We compared the lesion counts with those by radiologists using 2,846 images. Dice, lesion-wise sensitivity, and false discovery rate with full 5 contrasts were 0.698, 0.844, and 0.307, which improved to 0.767, 0.969, and 0.00 in large lesions (>100 voxels). The model using bootstrapping loss function provided a statistically significant increase of 7.1% in sensitivity and of 2.3% in Dice compared with the model using cross entropy loss. T1 post/pre-contrast and FLAIR were the most important contrasts. For large lesions, the 2D-UNet model trained using T1 pre-contrast, FLAIR, T2, PD had a lesion-wise sensitivity of 0.688 and false discovery rate 0.083, even without T1 post-contrast. For counting lesions in 2846 routine MRI images, the model with 2D-UNet and random forest, which was trained with bootstrapping cross entropy, achieved accuracy of 87.7% using T1 pre-contrast, T1 post-contrast, and FLAIR when lesion counts were categorized as 0, 1, and 2 or more. The model performs well in routine non-standardized MRI datasets, allows large-scale analysis of clinical datasets, and may have clinical applications.
format	article
author	Sibaji Gaj Daniel Ontaneda Kunio Nakamura
author_facet	Sibaji Gaj Daniel Ontaneda Kunio Nakamura
author_sort	Sibaji Gaj
title	Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
title_short	Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
title_full	Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
title_fullStr	Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
title_full_unstemmed	Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.
title_sort	automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical mri.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e8588fd874664c289c25174ec0b07f61
work_keys_str_mv	AT sibajigaj automaticsegmentationofgadoliniumenhancinglesionsinmultiplesclerosisusingdeeplearningfromclinicalmri AT danielontaneda automaticsegmentationofgadoliniumenhancinglesionsinmultiplesclerosisusingdeeplearningfromclinicalmri AT kunionakamura automaticsegmentationofgadoliniumenhancinglesionsinmultiplesclerosisusingdeeplearningfromclinicalmri
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Automatic segmentation of gadolinium-enhancing lesions in multiple sclerosis using deep learning from clinical MRI.

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