Search Results for author:
Found 5 papers, 0 papers with code
Individual Tooth Detection and Identification from Dental Panoramic X-Ray Images via Point-wise Localization and Distance Regularization
The accuracy of identification achieved a precision of 0. 997 and recall value of 0. 972.
Liver Segmentation in Abdominal CT Images via Auto-Context Neural Network and Self-Supervised Contour Attention
In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that shows high generalization performance and accuracy.
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation
The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.
Automatic Registration between Cone-Beam CT and Scanned Surface via Deep-Pose Regression Neural Networks and Clustered Similarities
The main significance of our study is twofold: 1) the employment of light-weighted neural networks which indicates the applicability of neural network in extracting pose cues that can be easily obtained and 2) the introduction of an optimal cluster-based registration method that can avoid metal artifacts during the matching procedures.
Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation
To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape.
