Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging Article Swipe
Rachana Sathish
,
Ronnie Rajan
,
Anusha Vupputuri
,
Nirmalya Ghosh
,
Debdoot Sheet
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/embc.2019.8857527
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/embc.2019.8857527
Ischaemic stroke is a medical condition caused by occlusion of blood supply to the brain tissue thus forming a lesion. A lesion is zoned into a core associated with irreversible necrosis typically located at the center of the lesion, while reversible hypoxic changes in the outer regions of the lesion are termed as the penumbra. Early estimation of core and penumbra in ischaemic stroke is crucial for timely intervention with thrombolytic therapy to reverse the damage and restore normalcy. Multisequence magnetic resonance imaging (MRI) is commonly employed for clinical diagnosis. However, a sequence singly has not been found to be sufficiently able to differentiate between core and penumbra, while a combination of sequences is required to determine the extent of the damage. The challenge, however, is that with an increase in the number of sequences, it cognitively taxes the clinician to discover symptomatic biomarkers in these images. In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI. The method employs recent developments in convolutional neural networks (CNN) for semantic segmentation in medical images. In the absence of availability of a large amount of labeled data, the CNN is trained using an adversarial approach employing cross-entropy as a segmentation loss along with losses aggregated from three discriminators of which two employ relativistic visual Turing test. This method is experimentally validated on the ISLES-2015 dataset through three-fold cross-validation to obtain with an average Dice score of 0.82 and 0.73 for segmentation of penumbra and core respectively.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/embc.2019.8857527
- OA Status
- green
- Cited By
- 1
- References
- 17
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2966223777
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2966223777Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/embc.2019.8857527Digital Object Identifier
- Title
-
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance ImagingWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-07-01Full publication date if available
- Authors
-
Rachana Sathish, Ronnie Rajan, Anusha Vupputuri, Nirmalya Ghosh, Debdoot SheetList of authors in order
- Landing page
-
https://doi.org/10.1109/embc.2019.8857527Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1908.01176Direct OA link when available
- Concepts
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Penumbra, Lesion, Convolutional neural network, Magnetic resonance imaging, Segmentation, Computer science, Artificial intelligence, Medicine, Radiology, Pattern recognition (psychology), Ischemia, Pathology, CardiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2019: 1Per-year citation counts (last 5 years)
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17Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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