Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement
Top Cited Papers
- 11 November 2022
- journal article
- research article
- Published by Elsevier in Computers in Biology and Medicine
- Vol. 152, 106296
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China
This publication has 18 references indexed in Scilit:
- TSASNet: Tooth segmentation on dental panoramic X-ray images by Two-Stage Attention Segmentation NetworkKnowledge-Based Systems, 2020
- Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke diseaseMedical Image Analysis, 2020
- Segmenting nailfold capillaries using an improved U-net networkMicrovascular Research, 2020
- A deep learning based medical image segmentation technique in Internet-of-Medical-Things domainFuture Generation Computer Systems, 2020
- IVD-Net: Intervertebral Disc Localization and Segmentation in MRI with a Multi-modal UNet2019
- Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectivesExpert Systems with Applications, 2018
- Squeeze-and-Excitation Networks2018
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teethComputer Methods and Programs in Biomedicine, 2017
- Teeth segmentation of dental periapical radiographs based on local singularity analysisComputer Methods and Programs in Biomedicine, 2014