Automated Segmentation of Cervical Intervertebral Disks from Videofluorography Using a Convolutional Neural Network and its Performance Evaluation

Abstract

Dysphagia has become an important issue in many countries, and there is a strong need for elucidating the causes of dysphagia. One of the promising ways is to analyze the static and dynamic mechanisms of cervical structures, such as epiglottises, hyoid bones, and cervical vertebral bodies, based on medical images. In this study, we propose an automated segmentation method of cervical intervertebral disks (IDs) from videofluorography (VF) by use of a convolutional neural network (CNN). First, cervical masks are extracted from the frame images of VF, and then a patch-based CNN is applied to the cervical masks to obtain the probability images of ID regions. The sizes of patches are changed in a certain range, and pixel values in the patches are normalized. Morphological image filters are applied to the probability images to eliminate false-positive pixels. The proposed method is applied to VF of 58 participants, consisting of 39 healthy people and 19 patients. The segmentation results are compared with the ground truth as determined by a medical doctor and are evaluated with the pixel-wise F-measure. The F-measure is highest (0.880) when the patch size is 21 × 21 pixels and the both of the pixel value normalization (PVN) and the false positive elimination (FPE) are applied. On the other hand, the F-measure is lowest (0.443) when the patch size is 15 × 15 pixels and neither PVN nor FPE is applied.