Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks

2019 ◽  
Author(s):  
Juan Pedro Vigueras Guillen ◽  
Hans G. Lemij ◽  
Jeroen van Rooij ◽  
Koenraad A. Vermeer ◽  
Lucas J. van Vliet
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Corneal Endothelium ◽  
Region Of Interest ◽  
Automatic Detection

scholarly journals Automatic detection of contouring errors using convolutional neural networks

2019 ◽  
Vol 46 (11) ◽  
pp. 5086-5097 ◽  
Author(s):  
Dong Joo Rhee ◽  
Carlos E. Cardenas ◽  
Hesham Elhalawani ◽  
Rachel McCarroll ◽  
Lifei Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection

scholarly journals Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jeong-Hoon Lee ◽  
Hee-Jin Yu ◽  
Min-ji Kim ◽  
Jin-Woo Kim ◽  
Jongeun Choi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Region Of Interest ◽  
Confidence Regions ◽  
Grand Challenge ◽  
Uncertainty Prediction ◽  
Integral Role ◽  
Roi Extraction ◽  
Aided Diagnosis

Abstract Background Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). Methods We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. Results Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. Conclusion Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

Deep Convolutional Neural Networks for Automatic Detection of Orbital Blowout Fractures

2020 ◽  
Vol 31 (2) ◽  
pp. 400-403
Author(s):  
Lunhao Li ◽  
Xuefei Song ◽  
Yucheng Guo ◽  
Yuchen Liu ◽  
Rou Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Deep Convolutional Neural Networks ◽  
Orbital Blowout Fractures

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

2016 ◽  
Vol 35 (5) ◽  
pp. 1182-1195 ◽  
Author(s):  
Qi Dou ◽  
Hao Chen ◽  
Lequan Yu ◽  
Lei Zhao ◽  
Jing Qin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Cerebral Microbleeds ◽  
Mr Images
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