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

Wenqi Li; Siyamalan Manivannan; Shazia Akbar; Jianguo Zhang; Emanuele Trucco; Stephen J. McKenna

doi:10.1109/ISBI.2016.7493530

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

Wenqi Li, Siyamalan Manivannan, Shazia Akbar, Jianguo Zhang, Emanuele Trucco, Stephen J. McKenna

Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

62 Citations (Scopus)

640 Downloads (Pure)

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 out-puts of CNN and HC-SVM together, a further performance boost is obtained.

Original language	English
Title of host publication	2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Subtitle of host publication	From Nano to Macro, ISBI 2016 - Proceedings
Publisher	IEEE
Pages	1405-1408
Number of pages	4
Volume	June-2016
ISBN (Electronic)	9781479923496
ISBN (Print)	9781479923502
DOIs	https://doi.org/10.1109/ISBI.2016.7493530
Publication status	Published - 16 Jun 2016
Event	2016 IEEE 13th International Symposium on Biomedical Imaging - Clarion Congress Hotel, Prague, Czech Republic Duration: 13 Apr 2016 → 16 Apr 2016 http://biomedicalimaging.org/2016/ (Link to Conference website)

Conference

Conference	2016 IEEE 13th International Symposium on Biomedical Imaging
Abbreviated title	ISBI 2016
Country/Territory	Czech Republic
City	Prague
Period	13/04/16 → 16/04/16
Internet address	http://biomedicalimaging.org/2016/ (Link to Conference website)

Keywords

Glands
Image segmentation
Support vector machines
Feature extraction
Dictionaries
Training
Neural networks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISBI.2016.7493530

Author Accepted ManuscriptAccepted author manuscript, 2.46 MB

Cite this

Li, W., Manivannan, S., Akbar, S., Zhang, J., Trucco, E., & McKenna, S. J. (2016). Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, ISBI 2016 - Proceedings (Vol. June-2016, pp. 1405-1408). IEEE. https://doi.org/10.1109/ISBI.2016.7493530

@inproceedings{e940cd620d854ad5859c82fd944956aa,

title = "Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks",

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 out-puts of CNN and HC-SVM together, a further performance boost is obtained.",

keywords = "Glands, Image segmentation, Support vector machines, Feature extraction, Dictionaries, Training, Neural networks",

author = "Wenqi Li and Siyamalan Manivannan and Shazia Akbar and Jianguo Zhang and Emanuele Trucco and McKenna, {Stephen J.}",

note = "S. Manivannan is supported by EPSRC grant EP/M005976/1 ; 2016 IEEE 13th International Symposium on Biomedical Imaging, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",

year = "2016",

month = jun,

day = "16",

doi = "10.1109/ISBI.2016.7493530",

language = "English",

isbn = "9781479923502",

volume = "June-2016",

pages = "1405--1408",

booktitle = "2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)",

publisher = "IEEE",

url = "http://biomedicalimaging.org/2016/",

}

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 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, ISBI 2016 - Proceedings. vol. June-2016, IEEE, pp. 1405-1408, 2016 IEEE 13th International Symposium on Biomedical Imaging, Prague, Czech Republic, 13/04/16. https://doi.org/10.1109/ISBI.2016.7493530

Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. / Li, Wenqi; Manivannan, Siyamalan; Akbar, Shazia et al.
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, ISBI 2016 - Proceedings. Vol. June-2016 IEEE, 2016. p. 1405-1408.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks

AU - Li, Wenqi

AU - Manivannan, Siyamalan

AU - Akbar, Shazia

AU - Zhang, Jianguo

AU - Trucco, Emanuele

AU - McKenna, Stephen J.

N1 - S. Manivannan is supported by EPSRC grant EP/M005976/1

PY - 2016/6/16

Y1 - 2016/6/16

N2 - 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 out-puts of CNN and HC-SVM together, a further performance boost is obtained.

AB - 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 out-puts of CNN and HC-SVM together, a further performance boost is obtained.

KW - Glands

KW - Image segmentation

KW - Support vector machines

KW - Feature extraction

KW - Dictionaries

KW - Training

KW - Neural networks

UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7493530&searchWithin%3Dgland+segmentation+in+colon%26filter%3DAND%28p_IS_Number%3A7493185%29

U2 - 10.1109/ISBI.2016.7493530

DO - 10.1109/ISBI.2016.7493530

M3 - Conference contribution

SN - 9781479923502

VL - June-2016

SP - 1405

EP - 1408

BT - 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

PB - IEEE

T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging

Y2 - 13 April 2016 through 16 April 2016

ER -

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

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this