Item


Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS) lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI’12). The Dice similarity coefficient (DSC) differences and the volume differences between the healthy and the simulated images are calculated for the subcortical structures and the brainstem. We observe that the three strategies are affected when lesions are present. However, the effects of the lesions do not follow the same pattern; the lesions either make the segmentation method underperform or surprisingly augment the segmentation accuracy. The obtained results show that FreeSurfer is the method most affected by the presence of lesions, with DSC differences (generated − healthy) ranging from−0.11 ± 0.54 to 9.65 ± 9.87, whereas FIRST tends to be the most robust method when lesions are present (−2.40 ± 5.54 to 0.44 ± 0.94). Lesion location is not important for global strategies such as FreeSurfer or majority voting, where structure segmentation is affected wherever the lesions exist. On the other hand, FIRST is more affected when the lesions are overlaid or close to the structure of analysis. The most affected structure by the presence of lesions is the nucleus accumbens (from −1.12 ± 2.53 to 1.32 ± 4.00 for the left hemisphere and from −2.40 ± 5.54 to 9.65 ± 9.87 for the right hemisphere), whereas the structures that show less variation include the thalamus (from 0.03 ± 0.35 to 0.74 ± 0.89 and from −0.48 ± 1.08 to −0.04 ± 0.22) and the brainstem (from −0.20 ± 0.38 to 1.03 ± 1.31). The three segmentation approaches are affected by the presence of MS lesions, which demonstrates that there exists a problem in the automatic segmentation methods of the deep gray matter (DGM) structures that has to be taken into account when using them as a tool to measure the disease progression

This work has been partially supported by “La Fundació la Marató de TV3” Ref. 201425 30, by Retos de Investigación TIN2014-55710-R and TIN2015-73563-JIN, and by MPC UdG 2016/022 grant

Elsevier

Manager: Ministerio de Economía y Competitividad (Espanya)
Author: González Villà, Sandra
Valverde Valverde, Sergi
Cabezas Grebol, Mariano
Pareto, Deborah
Vilanova Busquets, Joan Carles
Ramió Torrentà, Lluís
Rovira, Àlex
Oliver i Malagelada, Arnau
Lladó Bardera, Xavier
Date: 2018 June 5
Abstract: In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS) lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI’12). The Dice similarity coefficient (DSC) differences and the volume differences between the healthy and the simulated images are calculated for the subcortical structures and the brainstem. We observe that the three strategies are affected when lesions are present. However, the effects of the lesions do not follow the same pattern; the lesions either make the segmentation method underperform or surprisingly augment the segmentation accuracy. The obtained results show that FreeSurfer is the method most affected by the presence of lesions, with DSC differences (generated − healthy) ranging from−0.11 ± 0.54 to 9.65 ± 9.87, whereas FIRST tends to be the most robust method when lesions are present (−2.40 ± 5.54 to 0.44 ± 0.94). Lesion location is not important for global strategies such as FreeSurfer or majority voting, where structure segmentation is affected wherever the lesions exist. On the other hand, FIRST is more affected when the lesions are overlaid or close to the structure of analysis. The most affected structure by the presence of lesions is the nucleus accumbens (from −1.12 ± 2.53 to 1.32 ± 4.00 for the left hemisphere and from −2.40 ± 5.54 to 9.65 ± 9.87 for the right hemisphere), whereas the structures that show less variation include the thalamus (from 0.03 ± 0.35 to 0.74 ± 0.89 and from −0.48 ± 1.08 to −0.04 ± 0.22) and the brainstem (from −0.20 ± 0.38 to 1.03 ± 1.31). The three segmentation approaches are affected by the presence of MS lesions, which demonstrates that there exists a problem in the automatic segmentation methods of the deep gray matter (DGM) structures that has to be taken into account when using them as a tool to measure the disease progression
This work has been partially supported by “La Fundació la Marató de TV3” Ref. 201425 30, by Retos de Investigación TIN2014-55710-R and TIN2015-73563-JIN, and by MPC UdG 2016/022 grant
Document access: http://hdl.handle.net/2072/319821
Language: eng
Publisher: Elsevier
Rights: Attribution-NonCommercial-NoDerivs 4.0 Spain
Rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/es/
Subject: Multiple sclerosis
Esclerosi múltiple
Imatge -- Segmentació
Imaging segmentation
Imatges -- Processament -- Tècniques digitals
Image processing -- Digital techniques
Imatges -- Segmentació
Imaging segmentation
Imatgeria mèdica
Imaging systems in medicine
Title: Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation
Type: info:eu-repo/semantics/article
Repository: Recercat

Subjects

Authors


Warning: Unknown: write failed: No space left on device (28) in Unknown on line 0

Warning: Unknown: Failed to write session data (files). Please verify that the current setting of session.save_path is correct (/var/lib/php5) in Unknown on line 0