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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.  ...  Modeling ...................................... Y. Hiasa, Y. Otake, M. Takao, T. Ogawa, N. Sugano, and Y.  ... 
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  ...  S., TBME April 2020 1019-1029 Jeong, J., see Lee, M., TBME Nov. 2020 3151-3162 Jeong, J.S., see Sung, J.H., TBME Dec. 2020 3380-3391 Jerman, T., Chien, A., Pernus, F., Likar, B., and Spiclin, Z., Automated  ...  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.  ...  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  ...  By using our FCN, lung infection and normal regions are accurately segmented. Methods 3D FCN model for segmentation Encoder-decoder style FCNs such as U-Net are commonly used in segmentation.  ... 
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  
(analogue) interaction of participants from different disciplines and cultures.  ...  The traditional platforms of CARS Congresses for the scholarly publication and communication process for the presentation of R&D ideas were congress centers or hotels, typically hosting 600-800 participants  ...  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.  ...  Li, J., +, TBME June 2019 1549-1558 Slow-Wave Recordings From Micro-Sized Neural Clusters Using Multiwell Dilated-Inception Net: Multi-Scale Feature Aggregation for Cardiac Right Ventricle Segmentation  ... 
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
, personalized mobile health intervention, and computational model personalization.  ...  We start with a comprehensive tutorial of DRL, including the latest model-free and model-based algorithms.  ...  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  
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.  ...  comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI  ...  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  
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.  ...  A convolutional neural network with the U-Net architecture was used to train the segmentation model.  ...  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.  ...  Volumetric necrosis was obtained using prototype semi-automated segmentation software.  ... 
doi:10.1007/s13244-010-0011-1 pmid:23099631 pmcid:PMC3534347 fatcat:yfnq3lgteja2lfjl66ivdfnoum

Abstracts

2022 Journal of neuroendocrinology  
Results: The prevalence for NET was 5.056 persons (45% men, 94.2/100.000), and NEC (1.655 persons, 41% men, 30,8/100.000).  ...  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.  ... 
doi:10.1111/jne.13108 pmid:35253281 fatcat:rja6pngahbex5p6wqvxntlp3ty

15. Deutscher Wirbelsäulenkongress

2020 European spine journal  
2008 to 2016 (18.4 million persons) were used.  ...  Conclusion: We derived a fully automated planning tool for lumbosacral pedicle screws using AI.  ...  Methods: Between December 2012 and June 2018 clinical records were screened for patients admitted for 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  
risk factors using medical imaging as inputs.  ...  Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis.  ...  However, they are used in other relevant applications, such as the automation of image processing and segmentation for the generation of geometric models [11] .  ... 
doi:10.3390/app12083954 fatcat:so7cwudvyfgprigztufgw7we2e
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