Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation
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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Recent techniques are proposed using 3D CNN to fully benefit from the available information.
“Recent techniques are proposed using 3D CNN to fully benefit from the available information [89] [90].”
From Medical image analysis using convolutional neural networks: a review (2018) · cites this paper as [89]
Semantic-wise architectures are more susceptible to class imbalance but this can be solved by weighting the classes in the loss function.
“Semantic-wise architectures also are more susceptible to class imbalance but this can be solved by weighting the classes in the loss function [31].”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [31]
Brosch et al. [31] reported performance comparable to the state of the art methods and superior to the publicly available MS segmentation methods.
“They reported performance comparable to the state of the art methods and superior to the publicly available MS segmentation methods.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [31]
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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)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1229-1239
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- 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 · 2012: 1
- FWCI
- 40.95
- Type
- journal article
- Language
- en
- Open access
- closed
- 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
- Life Sciences › Neuroscience › Neurology › 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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