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MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning

Xiangyu Ma, Xinyuan Chen, Jingwen Li, Yu Wang, Kuo Men, Jianrong Dai
2021 Frontiers in Oncology  
A 16-layer U-Net was used to train the pCT generative model and a "pix2pix" generative adversarial network (GAN) was also trained to compare with the pure U-Net regrading pCT quality.  ...  Therefore, in this study, we developed a pseudo-CT (pCT) generation method to provide necessary ED information for MRI-only planning in NPC radiotherapy.MethodsTwenty patients with early-stage NPC who  ...  Patch-Based Generation of a Pseudo CT From Conventional MRI Sequences for MRI-Only Radiotherapy of the Brain.  ... 
doi:10.3389/fonc.2021.713617 pmid:34568044 pmcid:PMC8457879 fatcat:pi5giyh33ng3pcebpkrtddanuu

Deep learning‐based synthetic‐CT generation in radiotherapy and PET: a review

Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino, Joao Seco
2021 Medical Physics (Lancaster)  
The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported.  ...  Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones.  ...  Many CNN-based architectures have been proposed for image synthesis, with the most popular being the U-nets 51 and generative adversarial networks (GANs) 52 (see Figure 2 ).  ... 
doi:10.1002/mp.15150 pmid:34407209 fatcat:wbkjwjcrizgwph562mnhi5ylqq

Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review [article]

Tonghe Wang, Yang Lei, Yabo Fu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application.  ...  Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs and reported  ...  when compared with using MRI only or CT+MRI  ... 
arXiv:2004.10322v1 fatcat:bkhct7wzjnfrrd4kwa4rqw6rbe

Deep Learning Architectures and Techniques for Multi-organ Segmentation

Valentin Ogrean, Alexandru Dorobantiu, Remus Brad
2021 International Journal of Advanced Computer Science and Applications  
designsincluding "Generative Adversarial Networks" (GANs) or "Recurrent Neural Networks" (RNNs).  ...  Afterwards we present the most used multi-organ datasets, and we finalize by making a general discussion of current shortcomings and future potential research paths.  ...  The dice scores average between 0.91 and 0.98 for 6 covered organs. [45] Head and neck The authors present an architecture based on a 3D U-Net that is tested against a head and neck dataset.  ... 
doi:10.14569/ijacsa.2021.0120104 fatcat:6jlrngp3zjddtibucegydgjtuy

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis.  ...  Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges.  ...  Acknowledgments This project has received funding from the European Union's Horizon 2020 research and innovation pro-  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

Magnetic Resonance-Based Attenuation Correction and Scatter Correction in Neurological Positron Emission Tomography/Magnetic Resonance Imaging—Current Status With Emerging Applications

Jarmo Teuho, Angel Torrado-Carvajal, Hans Herzog, Udunna Anazodo, Riku Klén, Hidehiro Iida, Mika Teräs
2020 Frontiers in Physics  
Novel clinical and research applications where improved attenuation and scatter correction methods are beneficial are highlighted.  ...  In this review, we will summarize the past and current state-of-the-art developments in attenuation and scatter correction approaches for hybrid positron emission tomography (PET) and magnetic resonance  ...  Terhi Tuokkola was acknowledged for providing her clinical expertise, comments, and viewpoint for the manuscript.  ... 
doi:10.3389/fphy.2019.00243 fatcat:msnyzua32nbr5ihtvseqy6ey4a

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
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.  ...  Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of  ...  Compared to pseudo-3D networks, hybrid cascading 2D and 3D networks are more popular. Li et al. [60] proposed a hybrid densely connected U-Net (H-DenseUNet) for liver and liver-tumor segmentation.  ... 
arXiv:2009.13120v3 fatcat:ntgbqwkz55axrjum72elbm6rry

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful  ...  In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies.  ...  [91] addressed one-shot segmentation of brain structures from Magnetic Resonance Images (MRIs) with single atlas-based segmentation, where reversible voxel-wise correspondences between the atlas and  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Generation of Pseudo-CT using High-Degree Polynomial Regression on Dual-Contrast Pelvic MRI Data

Samuel C. Leu, Zhibin Huang, Ziwei Lin
2020 Scientific Reports  
(LOOCV) across all six patients, which is better than most previous results and comparable to another study using the more complicated atlas-based method.  ...  Here we propose a simple voxel method to generate the pseudo-CT (pCT) image using dual-contrast pelvic MRI data.  ...  Koike et al. 9 described a method to generate pCT images from T1w, T2w and fluid-attenuated inversion recovery MR images using an adversarial network for the head region.  ... 
doi:10.1038/s41598-020-64842-3 pmid:32415138 fatcat:rcggm3vhpvfmhbnlmbp46x6czy

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful  ...  In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies.  ...  [91] addressed one-shot segmentation of brain structures from Magnetic Resonance Images (MRIs) with single atlas-based segmentation, where reversible voxelwise correspondences between the atlas and  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges

Hala Shaari, Jasmin Kevrić, Samed Jukić, Larisa Bešić, Dejan Jokić, Nuredin Ahmed, Vladimir Rajs
2021 Brain Sciences  
Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.  ...  resonance imaging MRI / computed tomography CT) findings.  ...  A new patch-based technique using a CNNs for automatic brain MRI segmentation was suggested by another study in 2016 [55] .  ... 
doi:10.3390/brainsci11060716 pmid:34071202 fatcat:usmduuhzyzcsrh7lgto3ejzbfu

GAN-based generation of realistic 3D data: A systematic review and taxonomy [article]

André Ferreira, Jianning Li, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger
2022 arXiv   pre-print
Therefore, most of the publications on 3D Generative Adversarial Networks (GANs) are within the medical domain.  ...  We therefore outline GAN-based methods in these areas with common architectures, advantages and disadvantages.  ...  Acknowledgement This work received funding from enFaced (FWF KLI 678), enFaced 2.0 (FWF KLI 1044) and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative (https://kite.ikim.nrw/).  ... 
arXiv:2207.01390v1 fatcat:yny6btsy5zemjnbk7lnmxgsyzy

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  
There is an adversarial training as a learning method to train Generative Adversarial Network (GAN).  ...  Methods 2 D U-net, 2D SegNet, 2D Dense U-net, and 3D U-net were utilized as deep learning networks which are commonly used deep learning neural networks for anatomical structure segmentation in the medical  ...  Artificial intelligence coronary calcium scoring in low dose chest CT-Ready to go?  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

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  
With an increasing general demand and pressure on CARS to also go fully digital in the long term, many members of the CARS Congress Organizing Committee, however, are more cautious and convinced that one  ...  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  ...  This robot system can only measure the guidewire contact force, and it was only tested through phantom tests.  ... 
doi:10.1007/s11548-020-02171-6 pmid:32514840 fatcat:lyhdb2zfpjcqbf4mmbunddwroq

Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges

Gulshan Kumar, Hamed Alqahtani
2022 CMES - Computer Modeling in Engineering & Sciences  
Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases, including cancer disease.  ...  This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis, mainly focusing on breast cancer, brain cancer, skin cancer, and prostate cancer.  ...  Generative Adversarial Networks (GANs) A generative adversarial network was proposed by Goodfellow et al. [28] based on a twoplayer min-max game. It consists of two players.  ... 
doi:10.32604/cmes.2022.018418 fatcat:fwo2w6tp3jhzllw2g7ijnfe7hi
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