Fraz, M.M., Shaban, M., Graham, S. et al. (2 more authors) (2018) Uncertainty driven pooling network for microvessel segmentation in routine histology images. In: Stoyanov, D., Taylor, Z., Ciompi, F., Xu, Y., Martel, A., Maier-Hein, L., Rajpoot, N., van der Laak, J., Veta, M., McKenna, S., Snead, D., Trucco, E., Garvin, M.K., Chen, X.J. and Bogunovic, H., (eds.) Computational Pathology and Ophthalmic Medical Image Analysis. COMPAY 2018 : International Workshop on Computational Pathology, 16 Sep - 20 Oct 2018, Granada, Spain. Lecture Notes in Computer Science (11039). Springer Nature , pp. 156-164. ISBN 9783030009489
Abstract
Lymphovascular invasion (LVI) and tumor angiogenesis are correlated with metastasis, cancer recurrence and poor patient survival. In most of the cases, the LVI quantification and angiogenic analysis is based on microvessel segmentation and density estimation in immunohistochemically (IHC) stained tissues. However, in routine H&E stained images, the microvessels display a high level of heterogeneity in terms of size, shape, morphology and texture which makes microvessel segmentation a non-trivial task. Manual delineation of microvessels for biomarker analysis is labor-intensive, time consuming, irreproducible and can suffer from subjectivity among pathologists. Moreover, it is often beneficial to account for the uncertainty of a prediction when making a diagnosis. To address these challenges, we proposed a framework for microvessel segmentation in H&E stained histology images. The framework extends DeepLabV3+ by using an improved dice coefficient based custom loss function and also incorporating an uncertainty prediction mechanism. The proposed method uses an aligned Xception model, followed by atrous spatial pyramid pooling for feature extraction at multiple scales. This architecture counters the challenge of segmenting blood vessels of varying morphological appearance. To incorporate uncertainty, random transformations are introduced at test time for a superior segmentation result and simultaneous uncertainty map generation, highlighting ambiguous regions. The method is evaluated using 1167 images of size 512×512 pixels, extracted from 13 WSIs of oral squamous cell carcinoma (OSCC) tissue at 20x magnification. The proposed net-work achieves state-of-the-art performance compared to current semantic segmentation deep neural networks (FCN-8, U-Net, SegNet and DeepLabV3+).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 Springer Nature. This is an author-produced version of a paper subsequently published in COMPAY 2018 Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Microvessel detection; Tumor angiogenesis; Lymphovascular invasion; Separable convolution; Pyramid pooling based neural network; Uncertainty quantification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Oct 2019 11:15 |
Last Modified: | 24 Oct 2019 12:50 |
Status: | Published |
Publisher: | Springer Nature |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-00949-6_19 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150424 |