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Automatic prostate segmentation in cone-beam computed tomography images using rigid registration
2013
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Preserved Fulltext
Automatic segmentation of cone-beam computed tomography images for prostate cancer radiation therapy. Urology and Nephrology. Université de Valenciennes et du Hainaut-Cambresis, 2015. English. . ...
The calculus of variations (Euler-Lagrange equation) and the gradient descent method are used to find an optimal solution for Φ. ...
Appendix 2: Minimization of the cost function of the proposed DIR method The minimization of the total energy E is performed using the gradient descent which requires the computation of the gradient of ...
doi:10.1109/embc.2013.6610420
pmid:24110607
dblp:conf/embc/BoydevPDPTT13
fatcat:4y6dbhsywvgnbla6jaz3womcmu
Computer-Assisted Analysis of Biomedical Images
[article]
2021
arXiv
pre-print
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Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications. ...
This thesis aims at proposing novel and advanced computer-assisted methods for biomedical image analysis, also as an instrument in the development of Clinical Decision Support Systems, by always keeping ...
., a continuous version of the DSC) [418] in Eq. (6.3).
USE-Net and U-Net Using four scaling operations, U-Net and USE-Net were implemented on Keras with TensorFlow backend. ...
arXiv:2106.04381v1
fatcat:osqiyd3sbja3zgrby7bf4eljfm
Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review
[article]
2020
arXiv
pre-print
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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 ...
performances with related clinical applications on representative studies. ...
These weights and biases are called trainable parameters of networks. Gradient descent methods, such as Adam optimizer, are used to update trainable parameters of our networks. ...
arXiv:2004.10322v1
fatcat:bkhct7wzjnfrrd4kwa4rqw6rbe
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
[article]
2018
arXiv
pre-print
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Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. ...
In this article, we review the clinical reality of radiology and discuss the opportunities for application of deep learning algorithms. ...
Acknowledgments: The authors would like to acknowledge funding from the National Institutes of Biomedical Imaging and Bioengineering grant 5 R01 EB021360. ...
arXiv:1802.08717v1
fatcat:7qirj6hb2bdafnplc6au4wysqi
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
2018
Journal of Magnetic Resonance Imaging
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In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. ...
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. ...
Grant Support: The authors would like to acknowledge funding from the National Institutes of Biomedical Imaging and Bioengineering grant 5 R01 EB021360.
BIBLIOGRAPHY ...
doi:10.1002/jmri.26534
pmid:30575178
pmcid:PMC6483404
fatcat:7jg5sr7z6bbehd6xabsjw6bcde
AI-augmented histopathologic review using image analysis to optimize DNA yield and tumor purity from FFPE slides
[article]
2022
arXiv
pre-print
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A statistical analysis was performed to measure the impact of covariates on the results, offering insights on how to improve future applications of SmartPath. ...
Using digitized H&E-stained FFPE slides as inputs, SmartPath segments tumors, extracts cell-based features, and suggests macrodissection areas. ...
Tim Taxter for participating in the validation trial, and Matthew Kase for assistance in reviewing the text and figures. ...
arXiv:2203.13948v2
fatcat:e5427s4so5cqrpr2mhcycyyyue
The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy
2018
Frontiers in Oncology
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for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. ...
In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches ...
This work was supported in part by the National Institutes of Health P01 CA059827. ...
doi:10.3389/fonc.2018.00266
pmid:30101124
pmcid:PMC6072876
fatcat:3ypylnovujerlnar7ccuwuba7y
Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential
2022
Frontiers in Oncology
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Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, ...
Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality ...
with genomic data and revealed that the stochastic gradient descent model outperformed the best among the 12 methods. ...
doi:10.3389/fonc.2022.773840
pmid:35251962
pmcid:PMC8891653
fatcat:3h5tnm3aznb33k5ylkcd6tvs4e
ESGAR 2015 Book of Abstracts
2015
Insights into Imaging
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Among patients with rectal descent, 3 patients (16.7%) had a descent only during straining or defecation. ...
in rectal cancer: does it help in the assessment of tumor response? ...
Results: Tumor volume significantly correlates with pT and maximum tumor thickness with pT2 and pT3; none of these variables have shown a relation with pN. ...
doi:10.1007/s13244-015-0412-2
pmid:26104122
pmcid:PMC4485664
fatcat:pgp6jvdjkjho5mbxei2gi5lvja
Introduction to machine and deep learning for medical physicists
2020
Medical Physics (Lancaster)
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optimization methods for efficient training of these algorithms. ...
Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. ...
ACKNOWLEDGMENTS This work was supported in part by the National Institutes of ...
doi:10.1002/mp.14140
pmid:32418339
fatcat:b6jc2fta6zdp7pv7bjqw2st6zm
ESGAR 2011 Book of Abstracts
2011
Insights into Imaging
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MREC was performed with oral application of 5% manitol solution, rectal application of water and gadolinium injection. ...
Rectal application of a biphasic contrast agent is well tolerated and significantly improves detection of inflammation in three bowel segments, among them the terminal ileum, which is the segment most ...
doi:10.1007/s13244-011-0095-2
pmid:23100122
pmcid:PMC3533614
fatcat:4dfziojg5zfmhgf5iwbimggxqm
Cancer Diagnosis with the Aid of Artificial Intelligence Modeling Tools
2022
IEEE Access
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The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods. ...
Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. ...
Out of these methods the best accuracy, of 91.11%, is obtained by using the gradient descent method with variable learning rate and momentum and a mean square error of 0.112. [49] Arulmurugan and Anandakumar ...
doi:10.1109/access.2022.3152200
fatcat:imholi4vc5al3ml7b3tfghtkwu
Conference Guide [Front matter]
2020
2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)
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, and we use the modified U-net model to segment the rectal tumor region. ...
To overcome such difficulties, we propose a rectal tumor segmentation method by using a modified U-net, thereby improving the diagnostic efficiency and accuracy. ...
Security of consensus control is of key significance in multi-agent systems. ...
doi:10.1109/icarcv50220.2020.9305477
fatcat:4h7gpoj7ljgsrlkjoyw3qcfzxi
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
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The University Rovira i Virgili also supports this work with project 2019PFR-B2-61.
References ...
A graph clustering method in a proximal gradient descent framework was performed on the decimated data to segment the foreground, with background pixels removed. ...
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 ...
doi:10.1007/s11548-021-02375-4
pmid:34085172
fatcat:6d564hsv2fbybkhw4wvc7uuxcy
A Model for Online Interactive Remote Education for Medical Physics Using the Internet
2003
Journal of Medical Internet Research
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Search engines are generally classified into stochastic methods, such as simulated annealing or the genetic algorithm, and deterministic methods like the gradient descent or maximum likelihood methods. ...
For the same tumor dose delivered, the total number of MUs for a single IMRT fraction can be an order of magnitude greater than the MUs used for conventional treatments. ...
lp/mm with 12 bits of data. ...
doi:10.2196/jmir.5.1.e3
pmid:12746208
pmcid:PMC1550549
fatcat:tsyf45yusza2jiglkq62rgo3qm
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