Concepedia

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

Jordi Minnema, Maureen van Eijnatten, Allard A. Hendriksen, Niels Liberton, Daniël M. Pelt, Kees Joost Batenburg, Tymour Forouzanfar, Jan Wolff

Medical Physics · 2019 · 73 citations · 35 references

Open access

Abstract

The MS-D network was able to accurately segment bony structures in CBCT scans affected by metal artifacts.

References

35

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · 2016 · 214.9K citations · Full text

Image Classification, Deep Neural Networks, Machine Vision +14

ImageNet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM · 2017 · 75.5K citations · Full text

Convolutional Neural Network, Engineering, Machine Learning +19

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe · arXiv (Cornell University) · 2024 · 15.6K citations · Full text

Data Augmentation, Deep Neural Networks, Machine Vision +15

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Paul A. Yushkevich, Joseph Piven, Heather C. Hazlett et al. · NeuroImage · 2006 · 8.8K citations

Engineering, User-guided 3D, Active Contour Segmentation +18

3D Slicer as an image computing platform for the Quantitative Imaging Network

Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy–Cramer et al. · Magnetic Resonance Imaging · 2012 · 8.4K citations · Full text

Geometric Modeling, Image Analysis, Quantitative Imaging Network +12