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Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation

Yue Zhao, Lingming Zhang, Yang Liu, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen
2021 IEEE Transactions on Medical Imaging  
., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation.  ...  Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning.  ...  CONCLUSION A two-stream network, called TSGCN, has been proposed in this paper to segment individual teeth from the intra-oral scanner images acquired by intra-oral scanners.  ... 
doi:10.1109/tmi.2021.3124217 pmid:34714743 fatcat:neu67ww6i5esha7knh2hzgljke

Table of Contents

2022 IEEE Transactions on Medical Imaging  
Ghosh Two-Stream Graph Convolutional Network for Intra-Oral Scanner Image Segmentation .......................... .............................. Y. Zhao, L. Zhang, Y. Liu, D. Meng, Z. Cui, C. Gao, X.  ...  Wang DICDNet: Deep Interpretable Convolutional Dictionary Network for Metal Artifact Reduction in CT Images ... ............................................................... H. Wang, Y. Li, N.  ... 
doi:10.1109/tmi.2022.3160249 fatcat:aklm2gyfbfg2boor5n6ntm7e2i

Nuclei Glands Instance Segmentation in Histology Images: A Narrative Review [article]

Esha Sadia Nasir, Arshi Perviaz, Muhammad Moazam Fraz
2022 arXiv   pre-print
Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis.  ...  To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.  ...  Before arrival of convolutional neural networks, conventional nuclei segmentation methods were based on either geographical or statistical image features for seeds generation.  ... 
arXiv:2208.12460v1 fatcat:drl5p5cxtbadpcpjzoboxdxgnm

TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics [article]

Johnathan Pocock, Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Srijay Deshpande, Giorgos Hadjigeorghiou, Adam Shephard, Raja Muhammad Saad Bashir, Mohsin Bilal, Wenqi Lu, David Epstein, Fayyaz Minhas (+2 others)
2021 bioRxiv   pre-print
However, due to the sheer size and complexity of handling large multi-gigapixel whole-slide images, there is no open-source software library that provides a generic end-to-end API for pathology image analysis  ...  using best practices for CPath.  ...  The SlideGraph+ pipeline consists of patch extraction from WSI(s), stain normalization, node-level feature extraction, graph construction and prediction of the WSI label via a graph convolutional network  ... 
doi:10.1101/2021.12.23.474029 fatcat:pvhdbnv32vfuhl4y2wsg6rpw4u

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods [article]

Zohaib Salahuddin, Henry C Woodruff, Avishek Chatterjee, Philippe Lambin
2021 arXiv   pre-print
Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.  ...  for medical image analysis applications based on the type of generated explanations and technical similarities.  ...  Hence, the network learns a language inorder to communicate between the two modules. This method was validated on brain tumor segmentation in FLAIR images.  ... 
arXiv:2111.02398v1 fatcat:glrfdkbcqrbqto2nrl7dnlg3gq

CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020

2020 International Journal of Computer Assisted Radiology and Surgery  
The traditional platforms of CARS Congresses for the scholarly publication and communication process for the presentation of R&D ideas were congress centers or hotels, typically hosting 600-800 participants  ...  Aiming to stimulate complimentary thoughts and actions on what is being presented at CARS, implies a number of enabling variables for optimal analogue scholarly communication, such as (examples given are  ...  We thank NVIDIA for the Titan X hardware grant that allowed us to process the images in a faster way. ].  ... 
doi:10.1007/s11548-020-02171-6 pmid:32514840 fatcat:lyhdb2zfpjcqbf4mmbunddwroq

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
In this study, we developed three accurate OD segmentation models based on state-of-the-art deep convolutional neural networks (CNNs) for image segmentation.  ...  Using two thresholds for the Watershed transformation, the distance map is converted to two graphs.  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies  ...  imaging and positron emission tomography imaging.  ...  The rapid developments in hybrid imaging scanners (PET-CT, SPECT-CT, PET-MRI) has provided integrated images for diagnostic purposes.  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images.  ...  We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks.  ...  For instance, if the scanner pixel scale does not match with the powers of two for nuclei segmentation, custom neural networks with varying image sizes (e.g., 71 × 71) can be utilized (Saha et al., 2017  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Surround-View Cameras based Holistic Visual Perception for Automated Driving [article]

Varun Ravi Kumar
2022 arXiv   pre-print
such as geometric and semantic tasks using convolutional neural networks. 2) Using Multi-Task Learning to overcome computational bottlenecks by sharing initial convolutional layers between tasks and developing  ...  The dynamics changed from a primitive organism waiting for the food to come into contact for eating food being sought after by visual sensors.  ...  Graph Long Short-Term Memory (LSTM) [269] is a LSTM generalization from sequential data to general graph-structured data for semantic segmentation, primarily of people.  ... 
arXiv:2206.05542v1 fatcat:cdpn6afpvvf7hnsvry7cqbjq3u

Image computing for fibre-bundle endomicroscopy: A review [article]

Antonios Perperidis, Kevin Dhaliwal, Stephen McLaughlin, Tom Vercauteren
2018 arXiv   pre-print
there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for  ...  Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre- and post-processing algorithms, ranging  ...  Professor Dhaliwal is founder and shareholder of Edinburgh Molecular Imaging (Edinburgh, UK) and has in the past received funds for travel and meeting attendance from Mauna Kea Technologies (Paris, France  ... 
arXiv:1809.00604v1 fatcat:blwswhbjczft3lrbln7q5736km

Developing Techniques for Quantitative Renal Magnetic Resonance Imaging

Alexander Daniel, Susan Francis
2021 Zenodo  
Here a Convolutional Neural Network (CNN) is developed to generate fully automated masks of the kidneys to compute TKV with better than human precision.  ...  One such methodology is quantitative Magnetic Resonance Imaging (MRI).  ...  Neural Networks for Image Segmentation Methods This architecture is known as a Convolutional Neural Network (CNN) [36, 37] .  ... 
doi:10.5281/zenodo.5524888 fatcat:ba4f7zyabfckjlbtdyi6p5u3oy

Learning Capacity in Simulated Virtual Neurological Procedures

Mattia Samuel Mancosu, Silvester Czanner, Martin Punter
2020 Journal of WSCG  
ACKNOWLEDGEMENTS The authors would like to thank Oana Rotaru-Orhei for her comments and the three anonymous reviewers for their insightful suggestions.  ...  ACKNOWLEDGMENTS The authors acknowledge the support of the NSERC/Creaform Industrial Research Chair on 3-D Scanning for conducting the work presented in this paper.  ...  ., Semi-Supervised Classification with Graph Convolution Networks, in Conf. Proc. ICLR'17, 2017.  ... 
doi:10.24132/csrn.2020.3001.13 fatcat:uytlm7nytrhmnk553ellfhl54a

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
Conclusion A dental x-ray detector with improved spatial resolution within the range used in dental intra-oral imaging is feasible.  ...  Two experiments are performed: first, intra-patient 4D segmentation with a given initial segmentation for one time-point in a 4D sequence, and second, atlas-based segmentation of unseen patient data.  ...  Studies have shown that there is variation in the agreement between operators viewing the same tissue [1] suggesting that a complimentary technique for verification could improve the robustness of the  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

ESNR 2016

2016 Neuroradiology  
Imaging included DTI and anatomic T1 TFE.  ...  consecutive patients with radiologically or clinically isolated syndrome (n=23) primary or secondary progressive (PP/PS) MS (n=35), relapsing-remitting (RR) (n=24) MS, and 30 healthy controls on a 1.5T scanner  ...  We used our own scripts and the SPM12, Single Subject Gray Matter Networks and Brain Connectivity toolboxes for GM graph analyses.  ... 
doi:10.1007/s00234-016-1734-6 pmid:27578442 fatcat:ia2lasp4prfqxf7u2sh7hsyvem
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