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Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images [article]

Donghao Zhang, Siqi Liu, Shikha Chaganti, Eli Gibson, Zhoubing Xu, Sasa Grbic, Weidong Cai, Dorin Comaniciu
2020 arXiv   pre-print
Reconstructing the 3D geometric morphology of liver vessels from the contrasted CT images can enable multiple liver preoperative surgical planning applications.  ...  In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.  ...  In this paper, we propose a framework to reconstruct 3D vessel morphology from 3D multi-phase CT images by combining the fully convolutional neural network and the graph attention network.  ... 
arXiv:2003.07999v1 fatcat:ucw2thsl5vgqhk5zowghc4wvwm

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations [article]

Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua, Yassine Himeur
2022 arXiv   pre-print
We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously.  ...  Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation  ...  Acknowledgment This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation).  ... 
arXiv:2103.06384v2 fatcat:w6dxpyxhzzhs3gel25pgy6fqke

Evaluation of noise removal algorithms for imaging and reconstruction of vascular networks using micro-CT

Valentina Davidoiu, Lucas Hadjilucas, Irvin Teh, Nicolas P Smith, Jürgen E Schneider, Jack Lee
2016 Biomedical engineering and physics express  
The full evaluation pipeline included the reconstruction from projection images, denoising, vascular segmentation and graph model extraction to be performed on all simulated and real image data sets.  ...  -Dynamic PET reconstruction using temporal patch-based low rank penalty for ROI-based brain kinetic analysis Kyungsang Kim, Young Don Son, Yoram Bresler et al.  ...  Acknowledgments The authors would like to thank Dr D Aksentijevic for help with the rat heart preparation, A Vernet, V Thornton, Drs L Teboul and S Johnson for their help with the μCT-setup.  ... 
doi:10.1088/2057-1976/2/4/045015 fatcat:zwozpuccvbgbrcurzsmeep2yga

A review of vessel extraction techniques and algorithms

Cemil Kirbas, Francis Quek
2004 ACM Computing Surveys  
approaches, (5) neural network-based approaches, and (6) tube-like object detection approaches.  ...  We have divided vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based  ...  They demonstrate the result of their method applied to segment brain vessels from MRI/MRA and bronchi from a chest CT, and liver vessels from an abdominal CT.  ... 
doi:10.1145/1031120.1031121 fatcat:7zms3avnjrfd7plwcitqx3cdiq

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

Zheng Xu, Camilo Valdes, Jennifer Clarke
2018 Agronomy  
We review statistical and computational approaches that have been or may be effective for the analysis of 3D CT images of plant roots.  ...  We describe and comment on different approaches to aspects of the analysis of plant roots based on images, namely, (1) root segmentation, i.e., the isolation of root from non-root matter; (2) root-system  ...  Acknowledgments: We thank Stefan Gerth of Fraunhofer IIS for helpful discussions.  ... 
doi:10.3390/agronomy8050071 fatcat:crjmxrzncjap7hxg54nfkqxylq

Application of Image Processing and 3D Printing Technique to Development of Computer Tomography System for Automatic Segmentation and Quantitative Analysis of Pulmonary Bronchus

Chung Feng Jeffrey Kuo, Zheng-Xun Yang, Wen-Sen Lai, Shao-Cheng Liu
2022 Mathematics  
Afterwards, the micro bronchi with different radii were detected using morphological grayscale reconstruction to modify the initial airway.  ...  This study deals with the development of a computer tomography (CT) system for automatic segmentation and quantitative analysis of the pulmonary bronchus. It includes three parts.  ...  [18] proposed a graphics-based framework for reconstruction of the airway tree from CT scan images.  ... 
doi:10.3390/math10183354 fatcat:6nwygyzct5ghjp4zctkhciuome

Medical Image Segmentation Using Deep Learning: A Survey [article]

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi
2021 arXiv   pre-print
appropriate improvements in medical image segmentation based on deep learning approaches.  ...  For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions.  ...  [13] proposed a new mathematical morphology edge detection algorithm for lung CT images. Lalonde et al. [14] applied Hausdorff-based template matching to disc inspection, and Chen et al.  ... 
arXiv:2009.13120v3 fatcat:ntgbqwkz55axrjum72elbm6rry

Hierarchical imaging and computational analysis of three-dimensional vascular network architecture in the entire postnatal and adult mouse brain [article]

Thomas Wälchli, Jeroen Bisschop, Arttu Miettinen, Alexandra Ulmann-Schuler, Christoph Hintermueller, Eric P Meyer, Thomas Krucker, Regula Wälchli, Philippe Monnier, Peter Carmeliet, Johannes Vogel, Marco Stampanoni
2020 bioRxiv   pre-print
The entire protocol, from mouse perfusion to vessel network analysis, takes approximately 10 days.  ...  Resin-based vascular corrosion casting, scanning electron microscopy, synchrotron radiation and desktop uCT imaging, and computational network analysis are used.  ...  Computational reconstruction of the 3D vascular network In order to analyze the individual vessel branches, we converted the segmented image into a graph representing the vessel network.  ... 
doi:10.1101/2020.10.19.344903 fatcat:f5tjfp6dnfe3bjmrgqtgoub33e

Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications [article]

Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas
2022 arXiv   pre-print
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.  ...  Yet daunting challenges remain for designing the important image-to-graph transformation for multi-modality medical imaging and gaining insights into model interpretation and enhanced clinical decision  ...  Also, for liver lesion segmentation, a mutual information-based graph co-attention module (Mo et al., 2021) is proposed by extracting modality-specific features from T1-weighted images (T1WI) and establishing  ... 
arXiv:2202.08916v3 fatcat:zskcqvgjpnb6vdklmyy5rozswq

Medical image segmentation using deep learning: A survey

Risheng Wang, Tao Lei, Ruixia Cui, Bingtao Zhang, Hongying Meng, Asoke K. Nandi
2022 IET Image Processing  
appropriate improvements in medical image segmentation based on deep learning approaches.  ...  For supervised learning approaches, we analyse literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions.  ...  [13] proposed a new mathematical morphology edge detection algorithm for lung CT images. Lalonde et al. [14] applied Hausdorff-based template matching to disc inspection, and Chen et al.  ... 
doi:10.1049/ipr2.12419 fatcat:zvgj3vdzqbfbzjoglgmtnn6ukq

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  The direct use of CNNs for segmentation of vessels in 3D images encounters great challenges.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment  ...  With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical  ...  Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary  ... 
arXiv:2107.04537v4 fatcat:m5oqea5q6vhbhkerjmejder3hu

2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25

2021 IEEE journal of biomedical and health informatics  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, JBHI Feb. 2021 422-428 Attention-Guided Deep Neural Network With Multi-Scale Feature Fusion for Liver Vessel Segmentation.  ... 
doi:10.1109/jbhi.2022.3140980 fatcat:ufig7b54gfftnj3mocspoqbzq4

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be  ...  We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  Attempts using graph-based methods to perform 3D face reconstruction have been also explored.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Deep learning in medical imaging and radiation therapy

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods  ...  for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.  ...  patients F1:0.90 Vessel Carotid artery 150 CT 3D image patches Custom 3D CNN 455 patients fourfold CV 2.64 AE 4.98 mm Ascending aorta 139 3D US Custom CNN 719/150 patients 1.04 AE 0.50 mm Fetal anatomy  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm
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