Cited by 460Medical Image Segmentation TechniquesBrain Tumor Detection and ClassificationAdvanced Neural Network Applications

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Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

Brosch, Tom, Tang, Lisa Y. W., Yoo, Youngjin, Li, David K. B., Traboulsee, Anthony, Tam, Roger·2016·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2016.2528821

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Abstract

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

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Authors: Brosch, T., Tang, L. Y. W., Yoo, Y., Li, D. K. B., Traboulsee, A., & Tam, R.
Affiliations: University of British Columbia (CA)
ORCID: Brosch, Tom
Tang, Lisa Y. W.
Yoo, Youngjin
Li, David K. B.
Traboulsee, Anthony
Tam, Roger
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2016-05
Volume: 35
Issue: 5
Pages: 1229-1239
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2016.2528821
OpenAlex: W2342591535
Cited by: 460
References: 50
Citations by year: 2026: 4 · 2025: 24 · 2024: 32 · 2023: 42 · 2022: 50 · 2021: 63 · 2020: 63 · 2019: 67 · 2018: 54 · 2017: 46 · 2016: 14
FWCI: 40.95
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
Funders: Natural Sciences and Engineering Research Council of Canada
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: multiple sclerosis lesion segmentation, skip connections, magnetic resonance imaging, lesion size
MeSH terms: Machine Learning, Algorithms, Brain, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Multiple Sclerosis, Neural Networks, Computer, Imaging, Three-Dimensional

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Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

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