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)
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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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