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.
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