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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  
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]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
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

Zia Khan, Norashikin Yahya, Khaled Alsaih, Mohammed Isam Al-Hiyali, Fabrice Meriaudeau
2021 IEEE Access  
Focal loss balances the 818 classes and improved detection with post-processing and 819 dense conditional random field (SD-CRF).  ...  For more information, see 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]

Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile (+3 others)
2019 arXiv   pre-print
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

Deepa Darshini Gunashekar, Lars Bielak, Leonard Hägele, Benedict Oerther, Matthias Benndorf, Anca-L. Grosu, Thomas Brox, Constantinos Zamboglou, Michael Bock
2022 Radiation Oncology  
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

Farzad Khalvati, Junjie Zhang, Audrey G. Chung, Mohammad Javad Shafiee, Alexander Wong, Masoom A. Haider
2018 BMC Medical Imaging  
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

Henrik J. Michaely, Giacomo Aringhieri, Dana Cioni, Emanuele Neri
2022 Diagnostics  
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]

Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
2021 arXiv   pre-print
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]

Naji Khosravan, Haydar Celik, Baris Turkbey, Elizabeth Jones, Bradford Wood, Ulas Bagci
2018 arXiv   pre-print
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]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
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

Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
2021 Medical Image Analysis  
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/ pmid:34245943 fatcat:izmd6e4kdbd55ccjsfa7kpn4w4

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
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/ pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications [article]

Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan, Charles Huang, Hongyi Ren, Ruoxiu Xiao, Xiao Jia, Lei Xing
2019 Medical Physics (Lancaster)   pre-print
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

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
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  ...  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

Engin Dikici, John L. Ryu, Mutlu Demirer, Matthew Bigelow, Richard D. White, Wayne Slone, Barbaros Selnur Erdal, Luciano M. Prevedello
2020 IEEE journal of biomedical and health informatics  
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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