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A fully automated pipeline for brain structure segmentation in multiple sclerosis

  • Published:2020 Issue: Volume:27 Page:102306
  • ISSN:2213-1582
  • Container-title:NeuroImage: Clinical
  • language:en
  • Short-container-title:NeuroImage: Clinical

Author:

González-Villà Sandra,Oliver Arnau,Huo Yuankai,Lladó Xavier,Landman Bennett A.

Funder

by “La Fundació la Marató de TV3

Publisher

Elsevier BV

Subject

Cognitive Neuroscience,Clinical Neurology,Neurology,Radiology Nuclear Medicine and imaging

Reference31 articles.

1. A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights;Akhondi-Asl;IEEE Trans. Med. Image.,2014

2. Combination strategies in multi-atlas image segmentation: application to brain MR data;Artaechevarría;IEEE Trans. Med. Imag.,2009

3. Multi-atlas learner fusion: an efficient segmentation approach for large-scale data;Asman;Med. Image Anal.,2015

4. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain;Avants;Med. Image Anal.,2008

5. Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure;Commowick;Sci. Reports,2018

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3. FMTM-feature-map-based transform model for brain image segmentation in tumor detection;Network: Computation in Neural Systems;2022-12-13

4. BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images;International Journal of System Assurance Engineering and Management;2022-01-04

5. A Pipeline for Segmenting and Classifying Brain Lesions Caused by Stroke: A Machine Learning Approach;Information Systems and Technologies;2022
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