2026-04-14T03:02:21Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/144962024-05-22T09:49:38Zcom_2072_452955com_2072_2054col_2072_452957

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation González Villà, Sandra Valverde Valverde, Sergi Cabezas Grebol, Mariano Pareto, Deborah Vilanova, Joan Carles Ramió i Torrentà, Lluís Rovira, Àlex Oliver i Malagelada, Arnau Lladó Bardera, Xavier Ministerio de Economía y Competitividad (Espanya) 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 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 2017 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/10256/14496 http://hdl.handle.net/10256/14496 eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nicl.2017.05.003 info:eu-repo/semantics/altIdentifier/issn/2213-1582 info:eu-repo/grantAgreement/MINECO//TIN2014-55710-R/ES/HERRAMIENTAS DE NEUROIMAGEN PARA MEJORAR EL DIAGNOSIS Y EL SEGUIMIENTO CLINICO DE LOS PACIENTES CON ESCLEROSIS MULTIPLE/ info:eu-repo/grantAgreement/MINECO//TIN2015-73563-JIN/ES/SEGMENTACION AUTOMATICA DE LAS ESTRUCTURAS CEREBRALES PARA SU USO COMO BIOMARCADORES DE IMAGEN/ Attribution-NonCommercial-NoDerivs 4.0 Spain http://creativecommons.org/licenses/by-nc-nd/4.0/es/ info:eu-repo/semantics/openAccess application/pdf Elsevier NeuroImage: Clinical Volume, 2017, vol. 15,p. 228-238 Articles publicats (D-ATC)