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Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks

Li, W; Manivannan, S; Akbar, S; Zhang, J; Trucco, E; McKenna, SJ; (2016) Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. In: Kybic, J and Šonka, M, (eds.) ISBI 2016: 13th International Symposium on Biomedical Imaging: Proceedings. (pp. pp. 1405-1408). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

We investigate glandular structure segmentation in colon histology images as a window-based classification problem. We compare and combine methods based on fine-tuned convolutional neural networks (CNN) and hand-crafted features with support vector machines (HC-SVM). On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise Jaccard and Dice indices. For HC-SVM we further observe that training a second-level window classifier on the posterior probabilities - as an output refinement - can substantially improve the segmentation performance. The final performance of HC-SVM with refinement is comparable to that of CNN. Furthermore, we show that by combining and refining the posterior probability outputs of CNN and HC-SVM together, a further performance boost is obtained.

Type: Proceedings paper
Title: Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks
Event: ISBI 2016: 13th International Symposium on Biomedical Imaging, 13-16 April 2016, Prague, Czech Republic
ISBN-13: 9781479923502
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ISBI.2016.7493530
Publisher version: https://doi.org/10.1109/ISBI.2016.7493530
Language: English
Additional information: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Glands, Image segmentation, Support vector machines, Feature extraction, Dictionaries, Training, Neural networks
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
URI: https://discovery.ucl.ac.uk/id/eprint/1535835
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