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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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A Decomposable Model for the Detection of Prostate Cancer in Multi-Parametric MRI 609 Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension ...
in CTA images 517 Real-time augmented reality for ear surgery 518 Joint PET+MRI Patch-based Dictionary for Bayesian Random Field PET Reconstruction 520 Joint Prediction and Classification of Brain Image ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Artificial Intelligence in Quantitative Ultrasound Imaging: A Review
[article]
2020
arXiv
pre-print
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Therefore, it is in great need to develop automatic method to improve the imaging quality and aid in measurements in QUS. ...
Finally, challenges and future potential AI applications in QUS are discussed. ...
In 2018, Feng et al. proposed a 3D CNN to extract spatial-temporal features from CEUS sequential images for prostate cancer detection [84] . ...
arXiv:2003.11658v1
fatcat:iujuh7gra5ax7od2gxoo6yrbpe
Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review
2021
IEEE Access
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Focal loss balances the 818 classes and improved detection with post-processing and 819 dense conditional random field (SD-CRF). ...
For more information, see https://creativecommons.org/licenses/by-nc-detection of lesions with focal loss and dense 817 conditional random field (SD-CRF). ...
doi:10.1109/access.2021.3090825
fatcat:l2xe2tdwk5b6ldn7axvzbp5a5a
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1904.08254v1 fatcat:cg63h3qaevaadfcfmsqlxqa4xe
Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. ...
To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. ...
We thank the Cannizzaro Hospital, Catania, Italy, for providing one of the imaging datasets analyzed in this study.
References ...
arXiv:1904.08254v2
fatcat:voxb75goxbbtzchnrkk73k3s4y
Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology
2022
Radiation Oncology
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The CNN uses a U-Net architecture which was trained on multi-parametric MRI data from 122 patients to automatically segment the prostate gland and prostate tumor lesions. ...
AbstractAutomatic prostate tumor segmentation is often unable to identify the lesion even if multi-parametric MRI data is used as input, and the segmentation output is difficult to verify due to the lack ...
The support from MathWorks and the contribution of Arnie Berlin (The MathWorks Inc., Novi, MI, United States) in creation of new software and data processing techniques used in major parts of this work ...
doi:10.1186/s13014-022-02035-0
pmid:35366918
pmcid:PMC8976981
fatcat:gfjy2z7r7bfvxd45pwedzj7fii
MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection
2018
BMC Medical Imaging
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Quantitative radiomic features provide a plethora of minable data extracted from multi-parametric magnetic resonance imaging (MP-MRI) which can be used for accurate detection and localization of prostate ...
Conclusion: Quantitative radiomic features extracted from MP-MRI of prostate can be utilized to detect and localize prostate cancer. ...
Availability of data and materials The datasets during and/or analyzed during the current study available from the corresponding author on reasonable request pending the approval of the institution and ...
doi:10.1186/s12880-018-0258-4
pmid:29769042
pmcid:PMC5956891
fatcat:wu5xzil7anbfthr2u76v6m4uuu
Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review
2022
Diagnostics
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The intention of this review is to analyze the current value of biparametric prostate MRI in combination with methods of machine-learning and deep learning in the detection, grading, and characterization ...
The data show that detection of clinically significant prostate cancer and differentiation of prostate cancer from non-cancerous tissue using machine-learning and deep learning is feasible with promising ...
Therefore, it is increasing the number of potential parameters to the multi-parametric approach of prostate MRI and with potential benefits for PCA detection and grading and beyond. ...
doi:10.3390/diagnostics12040799
pmid:35453847
pmcid:PMC9027206
fatcat:g5ccvmzqqjh2tkdzugtwxm3o6y
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction
[article]
2021
arXiv
pre-print
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We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). ...
Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent ...
Acknowledgements The authors would like to acknowledge Maarten de Rooij and Ilse Slootweg from Radboud University Medical Center for the annotation of fully delineated masks of prostate cancer for every ...
arXiv:2101.03244v10
fatcat:l3nmcrhgsjfspjznv6urmotw7a
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
[article]
2018
arXiv
pre-print
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To do this, the C-CAD uses a deep learning algorithm in a newly designed multi-task learning platform to segment and diagnose cancers simultaneously. ...
The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve diagnostic decisions. ...
Specific to prostate cancer detection from radiology scans, recent works investigated the application of CNNs using multi-parametric MRI (Tsehay et al. (2017b) ) and a semi-supervised approach for biopsy-guided ...
arXiv:1802.06260v2
fatcat:3ufrgodf3va7dcsmii63mzunpe
Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear
[chapter]
2018
Lecture Notes in Computer Science
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for the Detection of Prostate Cancer in Multi-Parametric MRI
Nathan Lay*; Yohannes Tsehay; Yohan Sumathipala; Ruida Cheng; Sonia Gaur; Clayton Smith; Adrian
Barbu; Le Lu; Baris Turkbey; Peter Choyke ...
Radiotherapy Learning from Noisy Label Statistics: Detecting High Grade Prostate Cancer in Ultrasound Guided Biopsy Shekoofeh Azizi*; Pingkun Yan; Amir Tahmasebi; Peter Pinto; Bradley Wood; Jin Tae Kwak ...
T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen ...
doi:10.1007/978-3-030-00928-1_1
fatcat:ypoj3zplm5awljf6u5c2spgiea
End-to-end prostate cancer detection in bpMRI via 3D CNNs: effects of attention mechanisms, clinical priori and decoupled false positive reduction
2021
Medical Image Analysis
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We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model2 for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). ...
Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent ...
Acknowledgements The authors would like to acknowledge Maarten de Rooij and Ilse Slootweg from Radboud University Medical Center for the annotation of fully delineated masks of prostate cancer for every ...
doi:10.1016/j.media.2021.102155
pmid:34245943
fatcat:izmd6e4kdbd55ccjsfa7kpn4w4
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. ...
This research was funded by grants KUN 2012-5577, KUN 2014-7032, and KUN 2015 of the Dutch Cancer Society. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
[article]
2019
Medical Physics (Lancaster)
pre-print
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In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. ...
We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. ...
Yuan et al. 69 developed an effective multi-parametric MRI transfer learning for autonomous prostate cancer grading. ...
doi:10.1002/mp.13649
pmid:32418337
arXiv:1911.02521v1
fatcat:z6lbdtxxqzclthwu4mijo5ss3y
Front Matter: Volume 10575
2018
Medical Imaging 2018: Computer-Aided Diagnosis
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The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee. ...
Additional papers and presentation recordings may be available online in the SPIE Digital Library at SPIEDigitalLibrary.org. ...
excision after neoadjuvant chemoradiotherapy [10575-39]
10575 15
Development of a computer aided diagnosis model for prostate cancer classification on
multi-parametric MRI [10575-40]
10575 16 ...
doi:10.1117/12.2315758
fatcat:kqpt2ugrxrgx7m5rhasawarque
Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI
2020
IEEE journal of biomedical and health informatics
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Data is augmented extensively during training via a pipeline consisting of random ga mma correction and elastic deformation stages; the framework thereby maintains its invariance for a plausible range ...
BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. ...
step for detection of prostate cancer in multi-parametric MRI was illustrated by Yang et al ...
doi:10.1109/jbhi.2020.2982103
fatcat:mpe3pnzkvvaebgdrfqvabpp3au
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