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

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 -