A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.
Journal:
International journal of computer assisted radiology and surgery
Published Date:
Jan 16, 2019
Abstract
PURPOSE: Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer- and robot-aided interventions. Recent methods based on deep convolutional neural networks (CNN) have outperformed former heuristic methods. However, those methods were primarily evaluated on rigid, real-world environments. In this study, existing segmentation methods were evaluated for their use on a new dataset of transoral endoscopic exploration.