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Ministerio de Economía y Competitividad (Espanya) | |
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 |
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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 |
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http://hdl.handle.net/2072/299973 | |
eng | |
Elsevier | |
Attribution-NonCommercial-NoDerivs 4.0 Spain | |
http://creativecommons.org/licenses/by-nc-nd/4.0/es/ | |
Multiple sclerosis
Esclerosi múltiple Imatge -- Segmentació Imaging segmentation Imatges -- Processament -- Tècniques digitals Image processing -- Digital techniques Imatges -- Segmentació Imaging segmentation Imatges mèdiques Imaging systems in medicine |
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Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation | |
info:eu-repo/semantics/article | |
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