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Automated tissue segmentation of MR brain images in the presence of white matter lesions

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community

This work has been partially supported by ”La Fundacióla Maratóde TV3”, by Retos de Investigacin TIN2014-55710-R, and by the MPC UdG 2016/022 grant from the University of Girona

© Medical Image Analysis, 2017, vol. 35, p. 446-457

Elsevier

Director: Ministerio de Economía y Competitividad (Espanya)
Autor: Valverde Valverde, Sergi
Oliver i Malagelada, Arnau
Roura Perez, Eloy
González Villà, Sandra
Pareto, Deborah
Vilanova Busquets, Joan Carles
Ramió Torrentà, Lluís
Rovira, Àlex
Lladó Bardera, Xavier
Data: 1 gener 2017
Resum: Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community
This work has been partially supported by ”La Fundacióla Maratóde TV3”, by Retos de Investigacin TIN2014-55710-R, and by the MPC UdG 2016/022 grant from the University of Girona
Format: application/pdf
Cita: 025903
ISSN: 1361-8415 (versió paper)
1361-8423 (versió electrònica)
Accés al document: http://hdl.handle.net/10256/14119
Llenguatge: eng
Editor: Elsevier
Col·lecció: MINECO/PE 2015-2017/TIN2014-55710-R
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.media.2016.08.014
Articles publicats (D-ATC)
És part de: © Medical Image Analysis, 2017, vol. 35, p. 446-457
Drets: Tots els drets reservats
Matèria: Imatges -- Segmentació
Imaging segmentation
Imatges -- Processament -- Tècniques digitals
Image processing -- Digital techniques
Imatgeria mèdica
Imaging systems in medicine
Esclerosi múltiple -- Imatges
Multiple sclerosis -- Imaging
Esclerosi múltiple -- Imatges per ressonància magnètica
Multiple sclerosis -- Magnetic resonance imaging
Títol: Automated tissue segmentation of MR brain images in the presence of white matter lesions
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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