Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities

Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; van Uden, Inge W. M.; Sanchez, Clara I.; Litjens, Geert; de Leeuw, Frank-Erik; van Ginneken, Bram; Marchiori, Elena; Platel, Bram

doi:10.1038/s41598-017-05300-5

Cited by 237Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsBrain Tumor Detection and Classification

Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities

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Ghafoorian, Mohsen, Karssemeijer, Nico, Heskes, Tom, van Uden, Inge W. M., Sanchez, Clara I., Litjens, Geert, de Leeuw, Frank-Erik, van Ginneken, Bram +2 more·2017·Scientific Reports

DOI 10.1038/s41598-017-05300-5PMC PMC5505987

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Abstract

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).

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The improved segmentation performance of Ghafoorian et al. (2017) is mainly due to handcrafted features rather than the multi-scale paradigm.
“Noteworthy, the improved segmentation performance is mainly due to the contribution of handcrafted features rather than the multi-scale paradigm.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Ghafoorian et al. (2017)
Ghafoorian et al. (2017) achieved a 6% increase in Dice over their single scale baseline without handcrafted features on a test set of 46 cases.
“On a test set of 46 cases, their multi-scale architecture equipped with the hand-crafted features achieves 6% increase in Dice over their single scale baseline that does not incorporate the handcrafted features.”
From Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation (2020) · cites this paper as Ghafoorian et al. (2017)

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[29]Ghafoorian, M. et al. Transfer learning for domain adaptation in mri: Application in brain lesion segmentation. arXiv preprint arXiv:1702.07841 (2017). · arXiv
[89]Maas, A. L., Hannun, A. Y. & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. In Proc. ICML, vol. 30 (2013).

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Authors: Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uden, I. W. M., Sanchez, C. I., Litjens, G., de Leeuw, F.-E., van Ginneken, B., Marchiori, E., & Platel, B.
Affiliations: Radboud University Nijmegen (NL)
Radboud University Medical Center (NL)
Corresponding author: Ghafoorian, Mohsen
ORCID: Karssemeijer, Nico
Heskes, Tom
Litjens, Geert
Marchiori, Elena
Platel, Bram
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2017-07-11
Volume: 7
Issue: 1
Pages: 5110-5110
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-017-05300-5
PMC: PMC5505987
OpenAlex: W2532750509
Cited by: 236
References: 98
Citations by year: 2026: 7 · 2025: 18 · 2024: 18 · 2023: 18 · 2022: 30 · 2021: 41 · 2020: 25 · 2019: 32 · 2018: 33 · 2017: 12 · 2016: 1 · 2012: 1
FWCI: 11.58
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Radboud Repository (Radboud University) (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion, other-oa)
Funders: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016‐126‐351)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
Keywords: white matter hyperintensities, segmentation, brain MRI, deep convolutional neural networks, medical image analysis
MeSH terms: Aged, Aged, 80 and over, Decision Making, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neural Networks, Computer, Neuroimaging, White Matter

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