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Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling [article]

Yuta Hiasa, Yoshito Otake, Masaki Takao, Takeshi Ogawa, Nobuhiko Sugano, Yoshinobu Sato
2019 arXiv   pre-print
We propose a method for automatic segmentation of individual muscles from a clinical CT.  ...  The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label.  ...  Therefore, our focus in this study is to develop an automated method of segmentation of individual muscles for personalization of the musculoskeletal model. A.  ... 
arXiv:1907.08915v2 fatcat:ewaiugyn4vgb5gvjj2fk4h6oau

Table of contents

2020 IEEE Transactions on Medical Imaging  
Lediju Bell 1015 Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal .............................................................. M. Graham, F.  ...  Eldar 1051 Comparison of Objective Image Quality Metrics to Expert Radiologists' Scoring of Diagnostic Quality of MR Images ....... A. Mason, J. Rioux, S. E. Clarke, A. Costa, M. Schmidt, V.  ... 
doi:10.1109/tmi.2020.2980809 fatcat:blpr4ksdlfgnffqvgqazbptfqu

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
image segmentation to address the automation in identification and detection of the target regions or sub-regions.  ...  In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment  ...  Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary  ... 
arXiv:2107.04537v4 fatcat:m5oqea5q6vhbhkerjmejder3hu

2020 Index IEEE Transactions on Biomedical Engineering Vol. 67

2020 IEEE Transactions on Biomedical Engineering  
., A Time-Frequency Approach for Cerebral Embolic Load Monitoring; TBME April 2020 1007-1018 Imaduddin, S.M., Fanelli, A., Vonberg, F.W., Tasker, R.C., and Heldt, T., Pseudo-Bayesian Model-Based Noninvasive  ...  , Pernus, F., Likar, B., and Spiclin, Z., Automated Cutting Plane Positioning for Intracranial Aneurysm Quantification; TBME Feb. 2020 577-587 Ji, D., see Shim, S., TBME Sept. 2020 2497-2506 Jia, S.,  ...  Iterative Label Denoising Network: Segmenting Male Pelvic Organs in CT From 3D Bounding Box Annotations.  ... 
doi:10.1109/tbme.2020.3048339 fatcat:y7zxxew27fgerapsnrhh54tm7y

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  
Acknowledgements This work has been funded by the research project PI18/00169 from Instituto de Salud Carlos III & FEDER funds.  ...  We generated a stomach model from stomach region segmented from a CT volume.  ...  The pelvic bone used for the phantom was segmented from the CT of a patient using 3D Slicer platform.  ... 
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  
of the outstanding features of CARS is the personal (analogue) interaction of participants from different disciplines and cultures.  ...  A hybrid (analogue and digital) CARS 2020 has therefore been envisaged to take place at the University Hospital in Munich, with a balanced combination of analogue/personal and digital presentations and  ...  Acknowledgments This work was partly supported by a grant from Galgo Medical SL. We thank NVIDIA for the Titan X hardware grant that allowed us to process the images in a faster way.  ... 
doi:10.1007/s11548-020-02171-6 pmid:32514840 fatcat:lyhdb2zfpjcqbf4mmbunddwroq

2019 Index IEEE Transactions on Biomedical Engineering Vol. 66

2019 IEEE Transactions on Biomedical Engineering  
., +, TBME Feb. 2019 319-326 Sensitivity of Shoulder Musculoskeletal Model Predictions to Muscle-Tendon Properties.  ...  ., +, TBME Aug. 2019 2174-2181 Kinematics Sensitivity of Shoulder Musculoskeletal Model Predictions to Muscle-Tendon Properties.  ... 
doi:10.1109/tbme.2020.2964087 fatcat:mdfzsmdahnao5ccnuj232hycsm

Deep reinforcement learning in medical imaging: A literature review [article]

S. Kevin Zhou, Hoang Ngan Le, Khoa Luu, Hien V. Nguyen, Nicholas Ayache
2021 arXiv   pre-print
We start with a comprehensive tutorial of DRL, including the latest model-free and model-based algorithms.  ...  , personalized mobile health intervention, and computational model personalization.  ...  Using the searched U-Net, the segmentation performances on the medical segmentation decathlon (MSD) challenges are better than those of the nnU-Net approach Isensee et al. (2018), which is considered as  ... 
arXiv:2103.05115v1 fatcat:ocr6kq7atnbhxazj7twvvhl5uy

CARS 2016—Computer Assisted Radiology and Surgery Proceedings of the 30th International Congress and Exhibition Heidelberg, Germany, June 21–25, 2016

2016 International Journal of Computer Assisted Radiology and Surgery  
We would like to thank the Spanish company BQ for the donation of the 3D printing hardware for clinical use.  ...  Acknowledgements This study was supported in part by grants from the Ohio Department of Development (TVSF 15-  ...  An experimental phantom model, which used a sheep's solid organs (liver, kidney, spleen and heart) and muscles, was prepared for validation of our segmentation method and volume computation of the semi-automated  ... 
doi:10.1007/s11548-016-1412-5 pmid:27206418 fatcat:uk5r46n2xvhedkfjzmeiweyneq

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Hafsa Khalid, Muzammil Hussain, Mohammed A. Al Ghamdi, Tayyaba Khalid, Khadija Khalid, Muhammad Adnan Khan, Kalsoom Fatima, Khalid Masood, Sultan H. Almotiri, Muhammad Shoaib Farooq, Aqsa Ahmed
2020 Diagnostics  
The purpose of this research was to provide a "systematic literature review" of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by  ...  This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development.  ...  Researchers have demonstrated that u-net performed superiorly to the current cutting edge technique within clinically satisfactory runtimes.  ... 
doi:10.3390/diagnostics10080518 pmid:32722605 fatcat:mffkwokg4jdljkbyefvu7qmg54

SPR 2020

2020 Pediatric Radiology  
A convolutional neural network with the U-Net architecture was used to train the segmentation model.  ...  Conclusions: The U-Net models trained on our datasets accurately segment the abdominal muscle on pediatric axial CT scans across the range of ages 0-18 years.  ...  Clinical notes from the day of the procedure and longer term clinical follow up notes were used as a gold standard for complications.  ... 
doi:10.1007/s00247-020-04679-0 pmid:32435980 fatcat:y6da6d4blvaxlkus6w6zahpwlu

B - Scientific Sessions

2010 Insights into Imaging  
Methods and Materials: 20 patients with clinically suspected severe acute pancreatitis were referred for CT evaluation and imaged with a 320-slice CT unit within the first 24 hours after admission.  ...  Purpose: To evaluate perfusion differences between oedemic and necrotic pancreatic tissue in patients with initial stage of acute pancreatitis using 320-slice dynamic volume CT.  ...  A software running under Windows was developed for the automatic detection of left ventricular (LV) endocardial and epicardial contours on each MR image using a Bayesian flooding segmentation algorithm  ... 
doi:10.1007/s13244-010-0011-1 pmid:23099631 pmcid:PMC3534347 fatcat:yfnq3lgteja2lfjl66ivdfnoum

Abstracts

2022 Journal of neuroendocrinology  
Comparative biocomputational analysis of the splicing landscape across lung neuroendocrine neoplasms unveils a new actionable molecular layer .  ...  Materials and methods: We conducted a retrospective analysis of patients (pts) with type 1 g-NETs treated from 2000 to oct 2021. Endoscopy and CT scan were used for staging.  ...  Keywords: precision medicine, personalized treatment, neuroendocrine tumor, neuroendocrine carcinoma, molecular profile (F02) A systematic review of clinical prediction models for recurrence in patients  ... 
doi:10.1111/jne.13108 pmid:35253281 fatcat:rja6pngahbex5p6wqvxntlp3ty

15. Deutscher Wirbelsäulenkongress

2020 European spine journal  
Conclusion: We derived a fully automated planning tool for lumbosacral pedicle screws using AI.  ...  A vertebra instancebased approach employing a state-of-the-art U-Net framework was developed and trained followed by internal 5-fold cross-validation.  ...  The aim of this study is to identify risk factors for obese patients suffering from spondylodiscitis.  ... 
doi:10.1007/s00586-020-06630-1 pmid:33078266 fatcat:2e63m5xi7zfmrjjoxffwn5pq5m

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Johane H. Bracamonte, Sarah K. Saunders, John S. Wilson, Uyen T. Truong, Joao S. Soares
2022 Applied Sciences  
Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based  ...  Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12083954 fatcat:so7cwudvyfgprigztufgw7we2e
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