[HTML][HTML] Quantitative analysis of skeletal muscle by computed tomography imaging—State of the art

K Engelke, O Museyko, L Wang, JD Laredo - Journal of orthopaedic …, 2018 - Elsevier
The radiological assessment of muscle properties—size, mass, density (also termed
radiodensity), composition, and adipose tissue infiltration—is fundamental in muscle …

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives

M Rozynek, I Kucybała, A Urbanik, W Wojciechowski - Nutrition, 2021 - Elsevier
Sarcopenia is a muscle disease which previously was associated only with aging, but in
recent days it has been gaining more attention for its predictive value in a vast range of …

Automated muscle segmentation from clinical CT using Bayesian U-Net for personalized musculoskeletal modeling

Y Hiasa, Y Otake, M Takao, T Ogawa… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …

Machine learning for automatic paraspinous muscle area and attenuation measures on low-dose chest CT scans

R Barnard, J Tan, B Roller, C Chiles, AA Weaver… - Academic radiology, 2019 - Elsevier
Rationale and Objectives To develop and evaluate an automated machine learning (ML)
algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) …

Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total …

D Wu, X Zhi, X Liu, Y Zhang, W Chai - Journal of Orthopaedic Surgery and …, 2022 - Springer
Purpose Preoperative three-dimensional planning is important for total hip arthroplasty. To
simulate the placement of joint implants on computed tomography (CT), pelvis and femur …

Image object detection and semantic segmentation based on convolutional neural network

L Zhang, Z Sheng, Y Li, Q Sun, Y Zhao… - Neural Computing and …, 2020 - Springer
In this paper, an unsupervised co-segmentation algorithm is proposed, which can be
applied to the image with multiple foreground objects simultaneously and the background …

Deformable image registration based on single or multi-atlas methods for automatic muscle segmentation and the generation of augmented imaging datasets

WH Henson, C Mazzá, E Dall'Ara - PLoS One, 2023 - journals.plos.org
Muscle segmentation is a process relied upon to gather medical image-based muscle
characterisation, useful in directly assessing muscle volume and geometry, that can be used …

Muscle volume quantification: guiding transformers with anatomical priors

L Piecuch, V Gonzales Duque, A Sarcher… - … workshop on shape in …, 2023 - Springer
Muscle volume is a useful quantitative biomarker in sports, but also for the follow-up of
degenerative musculo-skelletal diseases. In addition to volume, other shape biomarkers can …

Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross …

W Choi, CH Kim, H Yoo, HR Yun, DW Kim, JW Kim - BMJ open, 2024 - bmjopen.bmj.com
Objectives We aimed to develop an automated method for measuring the volume of the
psoas muscle using CT to aid sarcopenia research efficiently. Methods We used a data set …

Assembling a Learnable Mumford–Shah Type Model with Multigrid Technique for Image Segmentation

J Meng, W Guo, J Liu, M Yang - SIAM Journal on Imaging Sciences, 2024 - SIAM
The classical Mumford–Shah (MS) model has been successful in some medical image
segmentation tasks, providing segmentation results with smooth boundaries of objects …