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Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy [chapter]

Kibrom Berihu Girum, Gilles Créhange, Raabid Hussain, Paul Michael Walker, Alain Lalande
2019 Lecture Notes in Computer Science  
In this paper, we present a fully automatic deep generative model-driven multimodal prostate segmentation method using convolutional neural network (DGMNet).  ...  For example, in radiotherapy automatic prostate segmentation is essential in prostate cancer diagnosis, therapy, and post-therapy assessment from T2-weighted MR or CT images.  ...  In this paper, we present a new deep generative model-driven anatomical structure segmentation (named DGMNet), specifically designed for multimodal (CT and MR) prostate segmentation.  ... 
doi:10.1007/978-3-030-32486-5_15 fatcat:y5fj3e4bajcrxjv4lfthxr4deu

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Modeling and Inverse Imaging of Cardiac Transmembrane Potential 427 Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation 428 Deep Attentional Features for Prostate Segmentation in  ...  Trauma Interventions 753 Multimodal Recurrent Model with Attention for Automated Radiology Report Generation 754 Structured Deep Generative Model of FMRI Signals for Mental Disorder Diagnosis 758 The  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data [article]

Tobias Fechter, Ilias Sachpazidis, Dimos Baltas
2022 arXiv   pre-print
However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase.  ...  In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly presented in others.  ...  [123] used a U-net model to generate prostate contours for driving the registration of PET/CT and TRUS images, whereas the registration process itself was not deep learning based.  ... 
arXiv:2205.07516v2 fatcat:r62s65vbwrcjda5cbm2eclghbi

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen
2017 Contrast Media & Molecular Imaging  
Hence, in this paper, we review the applications of deep learning in molecular imaging in terms of tumor lesion segmentation, tumor classification, and survival prediction.  ...  We also outline some future directions in which researchers may develop more powerful deep learning models for better performance in the applications in cancer molecular imaging.  ...  Generally, deep learning models achieved best performance in HGG segmentation.  ... 
doi:10.1155/2017/9512370 pmid:29114182 pmcid:PMC5661078 fatcat:ev3zrlx67vfo5mt23e5u3y2t64

Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine [chapter]

Alexander F.I. Osman
2019 Machine Learning in Medicine and Biology [Working Title]  
In this chapter, we provide the interested reader with an overview of the ongoing advances and cutting-edge applications of state-of-the-art ML techniques in radiation oncology process from the radiotherapy  ...  Machine learning (ML) applications in medicine represent an emerging field of research with the potential to revolutionize the field of radiation oncology, in particular.  ...  Most of the clinical aspects provided in this chapter were based on the author's knowledge and experience gained during his residency at AUBMC. The contents are solely representing the author's view.  ... 
doi:10.5772/intechopen.84629 fatcat:tobl67e5qvgf5mat43ex5lounq

Multimodality Biomedical Image Registration using Free Point Transformer Networks [article]

Zachary M. C. Baum, Yipeng Hu, Dean C. Barratt
2020 arXiv   pre-print
In a multimodal registration task using prostate MR and sparsely acquired ultrasound images, FPT yields comparable or improved results over other rigid and non-rigid registration methods.  ...  The point transformer module assumes no vicinity or smoothness in predicting spatial transformation and, together with the global feature extractor, is trained in a data-driven fashion with an unsupervised  ...  In this work, we present a novel deep neural network architecture for data-driven, non-rigid point-set registration.  ... 
arXiv:2008.01885v1 fatcat:yjd423z3afbn3nbu4z3ul6kgv4

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives

Octavian Sabin Tătaru, Mihai Dorin Vartolomei, Jens J. Rassweiler, Oșan Virgil, Giuseppe Lucarelli, Francesco Porpiglia, Daniele Amparore, Matteo Manfredi, Giuseppe Carrieri, Ugo Falagario, Daniela Terracciano, Ottavio de Cobelli (+3 others)
2021 Diagnostics  
Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare.  ...  Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients.  ...  AI in Prostate Cancer ML Approach in TRUS Studies for Treatment of PCa For brachytherapy, ultrasound studies on prostate segmentation, a deeply supervised deep learning-based approach [78] , an efficient  ... 
doi:10.3390/diagnostics11020354 pmid:33672608 pmcid:PMC7924061 fatcat:jqktyzjrhjh2jaxvlpk3pomube

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
2022 Frontiers in Oncology  
In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation  ...  The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are  ...  Generally, multimodality and/or multimodal radiomics models have superior survival prediction capabilities th an single-m oda lity or s ingle-moda l radiomics models.  ... 
doi:10.3389/fonc.2022.773840 pmid:35251962 pmcid:PMC8891653 fatcat:3h5tnm3aznb33k5ylkcd6tvs4e

Can exercise suppress tumour growth in advanced prostate cancer patients with sclerotic bone metastases? A randomised, controlled study protocol examining feasibility, safety and efficacy

Nicolas H Hart, Robert U Newton, Nigel A Spry, Dennis R Taaffe, Suzanne K Chambers, Kynan T Feeney, David J Joseph, Andrew D Redfern, Tom Ferguson, Daniel A Galvão
2017 BMJ Open  
Whole-body and appendicular segmentations will be generated in accordance with Hart and colleagues. 57 Fat area (Fa.Ar) and muscle density (Mu.Den) of the thigh and shank segments will be measured using  ...  The study aims to (1) establish the feasibility and safety of a combined modular multimodal exercise programme with spinal isometric training in advanced prostate cancer patients with sclerotic bone metastases  ... 
doi:10.1136/bmjopen-2016-014458 pmid:28559456 pmcid:PMC5777463 fatcat:2jgvaikbpffzzgbmd443zcccca

Front Matter: Volume 9415

Proceedings of SPIE, Robert J. Webster, Ziv R. Yaniv
2015 Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling  
Numbers in the index correspond to the last two digits of the six-digit citation identifier (CID) article numbering system used in Proceedings of SPIE.  ...  interventions [9415-33] 9415 10 4DCBCT-based motion modeling and 3D fluoroscopic image generation for lung cancer radiotherapy [9415-34] 9415 11 Surgical tool detection and tracking in retinal microsurgery  ...  radiation SESSION 8 SEGMENTATION 9415 14 Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images [9415-38] 9415 15 Grading remodeling severity  ... 
doi:10.1117/12.2184297 dblp:conf/miigp/X15 fatcat:qx5pgjakdfg75mpufw66xc5k5e

Medical Image Registration Using Deep Neural Networks: A Comprehensive Review [article]

Hamid Reza Boveiri, Raouf Khayami, Reza Javidan, Ali Reza MehdiZadeh
2020 arXiv   pre-print
In this paper, a comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented.  ...  This review allows a deep understanding and insight for the readers active in the field who are investigating the state-of-the-art and seeking to contribute the future literature.  ...  GAN SmartTarget -MRI-T2 and TRUS (Prostate) - Multimodal TRE and Dice Directly regressing the multimodal deformable registration via a weakly- supervised anatomical-label- driven GAN Hu et  ... 
arXiv:2002.03401v1 fatcat:u4utrifr2rg3bf6x6fgohyfmpy

Tasks for artificial intelligence in prostate MRI

Mason J. Belue, Baris Turkbey
2022 European Radiology Experimental  
Impact (FWCI), dive into some of the top AI models for segmentation, detection, and classification.  ...  AI carries a vast number of potential applications in every step of the prostate cancer diagnostic pathway from classifying/improving prostate multiparametric magnetic resonance image quality, prostate  ...  AI-based detection models may range from two-class Fig. 2 The potential impact of AI-driven prostate segmentation on the workflow of the radiologist.  ... 
doi:10.1186/s41747-022-00287-9 pmid:35908102 pmcid:PMC9339059 fatcat:zj4jooecznhjfmh4l5don74wxq

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

James T. T. Coates, Giacomo Pirovano, Issam El Naqa
2021 Journal of Medical Imaging  
The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together  ...  Discussion then focuses on uses of conventional and deep machine learning in radiomics.  ...  Variants of autoencoders can be used in the context of generative models (deep encoders) and have only recently found applications in feature extractionbased frameworks as well as for segmentation.  ... 
doi:10.1117/1.jmi.8.3.031902 pmid:33768134 pmcid:PMC7985651 fatcat:y4djrrysrbbifcm5dz6gbjsumu

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Ahmad Chaddad, Michael Kucharczyk, Abbas Cheddad, Sharon Clarke, Lama Hassan, Shuxue Ding, Saima Rathore, Mingli Zhang, Yousef Katib, Boris Bahoric, Gad Abikhzer, Stephan Probst (+1 others)
2021 Cancers  
models.  ...  challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive  ...  This pipeline model was expanded by Chaddad et al., adapting multiple 2D CNN models to generate deep texture features in prostatic mpMRIs, generating a robust model for predicting the GS [88] .  ... 
doi:10.3390/cancers13030552 pmid:33535569 pmcid:PMC7867056 fatcat:tsyj3gt4mjax5cad5xxyxuo4jy

Radiomics and Prostate MRI: Current Role and Future Applications

Giuseppe Cutaia, Giuseppe La Tona, Albert Comelli, Federica Vernuccio, Francesco Agnello, Cesare Gagliardo, Leonardo Salvaggio, Natale Quartuccio, Letterio Sturiale, Alessandro Stefano, Mauro Calamia, Gaspare Arnone (+2 others)
2021 Journal of Imaging  
In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular  ...  We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.  ...  Deep learning algorithms have been applied in automatic segmentation of the prostate gland [46, 47] with potential benefit for patient management personalization.  ... 
doi:10.3390/jimaging7020034 pmid:34460633 pmcid:PMC8321264 fatcat:doga4lljhzbrfdwv5yuylgyjdq
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