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
2016 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)  
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
more » ... n 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.
doi:10.1109/isbi.2016.7493530 dblp:conf/isbi/LiMAZTM16 fatcat:2cnohhllqngopog7fwpstek6nq