scholarly journals Thigh muscle segmentation using a hybrid FRFCM‐based multi‐atlas method and morphology‐based interpolation algorithm

2021 ◽  
Author(s):  
Malihe Molaie ◽  
Reza Aghaeizadeh Zoroofi
Keyword(s):  
Thigh Muscle ◽  
Interpolation Algorithm ◽  
Atlas Method ◽  
Muscle Segmentation

scholarly journals Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0198200 ◽  
Author(s):  
Sarah Schlaeger ◽  
Friedemann Freitag ◽  
Elisabeth Klupp ◽  
Michael Dieckmeyer ◽  
Dominik Weidlich ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Chemical Shift ◽  
Magnetic Resonance Images ◽  
Thigh Muscle ◽  
Reference Database ◽  
Muscle Segmentation

scholarly journals Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat–water decomposition MRI

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Ding ◽  
Peng Cao ◽  
Hing-Chiu Chang ◽  
Yuan Gao ◽  
Sophelia Hoi Shan Chan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Qualitative Assessment ◽  
Thigh Muscle ◽  
Reference Database ◽  
Automated Segmentation ◽  
Manual Segmentation ◽  
Water Decomposition ◽  
Fat Infiltration ◽  
Fat Fraction ◽  
Muscle Segmentation

Abstract Background Time-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat–water decomposition MRI. Results This study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs. Conclusions This automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.

Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method

2018 ◽  
Vol 13 (7) ◽  
pp. 977-986 ◽  
Author(s):  
Futoshi Yokota ◽  
Yoshito Otake ◽  
Masaki Takao ◽  
Takeshi Ogawa ◽  
Toshiyuki Okada ◽  
...  
Keyword(s):  
Ct Images ◽  
Atlas Method ◽  
Muscle Segmentation

Morpho-functional features of rabbit's thyroid gland with its autotransplantation in the thigh muscle

2013 ◽  
Author(s):  
Igor V Makarov ◽  
Tatyana A Fedorina ◽  
Larisa T Volova ◽  
Anastasia V Bodrova
Keyword(s):  
Thyroid Gland ◽  
Thigh Muscle ◽  
Functional Features

Ideal body position for epinephrine autoinjector administration

2020 ◽  
Author(s):  
Qing Wang ◽  
Luke Pittman ◽  
Andrew Healey ◽  
James Chang ◽  
T. Ted Song
Keyword(s):  
Body Position ◽  
Vastus Lateralis ◽  
The United States ◽  
Thigh Muscle ◽  
First Line ◽  
Medical Treatment Facility ◽  
First Line Therapy ◽  
Epinephrine Autoinjector ◽  
Ideal Body ◽  
The Ideal

Background: Epinephrine is the first-line therapy for patients with anaphylaxis, and intramuscular (IM) delivery is shownto be superior to subcutaneous (SC) delivery. There currently is no consensus on the ideal body position for epinephrine autoinjector (EAI) administration.Objective: We designed this study to investigate whether SC tissue depth (SCTD) is affected by body position (e.g., standing, sitting, supine), which can potentially impact delivery of EAI into the IM space.Methods: Volunteer adults (ages >/= 18 years) from a military medical treatment facility in the United States were recruitedto participate in this study. SCTD of the vastus lateralis was measured via ultrasound at standing, sitting, and supine bodypositions. Subjects’ age, sex, and body mass index (BMI) were collected. Statistical analysis was performed to compare averageSCTD between body positions, sex, and BMI.Results: An analysis of variance of 51 participants (33 men and 18 women) did not reveal statistically significant differencein SCTD among standing, sitting, and supine body positions. It did show a significantly greater SCTD in women than in men (2.72 +/- 1.36 cm versus 1.10 +/- 0.38 cm; p < 0.001). There was no significant association observed between BMI and SCTD in this study.Conclusion: Body position did not seem to significantly change the distance between skin and thigh muscle in adults. Thiswould suggest that there might not be an ideal body position for EAI administration. Therefore, in case of anaphylaxis, promptadministration of epinephrine is recommended at any position.

Sign in / Sign up

Export Citation Format

Share Document