Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection.

Dysphagia has a large impact on the society because it is a risk factor of malnutrition and aspiration pneumonia, and therefore, it is necessary to elucidate the entire mechanism of dysphagia. In this study, we propose a segmentation method of cervical intervertebral disks (CIDs) in videofluorography (VF) by use of patch-based convolutional neural network (CNN), our multi-channelization (MC) method and image feature selection.
Twenty image filters are individually applied to a VF frame image to generate feature images. One color image, called a multi-channelized image, is generated by setting three selected feature images to its red, green and blue channels. Patch-based CNN is applied to the MC image, and the segmentation accuracy of CIDs is evaluated by the pixel-based F-measure. The combination of the three feature images is optimized by the simulated annealing method.
The proposed method was applied to actual VF dataset consisting of 19 patients and 39 healthy participants. The segmentation accuracy was 59.3% in the F-measure when Sobel and morphological top-hat filters were selected in MC, whereas it was 56.2% when original frame images were used.
The experimental results demonstrated that the proposed method was able to segment CIDs from actual VF and also that the MC method was able to increase the segmentation accuracy by approximately 3%. In this study, LeNet was used as CNN. One of our future tasks is to use other CNNs.