Semi-automated Tracing of Hamstring Muscle Architecture for B-mode Ultrasound Images

Cronin, K.; Delahunt, Eamonn; Foley, Shane; Vito, Giuseppe De; McCarthy, Conor; Cournane, Seán

doi:10.1055/a-1493-3082

Cited by 7 publications

(27 citation statements)

References 30 publications

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“…Recently, another study examined ultrasound assessed muscle architecture, but of the gastrocnemius medialis (GM) and gastrocnemius lateralis (GL) and reported moderate to excellent reliability (ICC: GM = 0.63–0.91, GL = 0.63–0.82) ( May, Locke & Kingsley, 2021 ). Furthermore, automatic ultrasound analysis tools, have been recently developed ( Cronin et al, 2021 ; Seynnes & Cronin, 2020 ); however, the literature evaluating the reliability of the automatic programs are sparse. In the current study, automatic ultrasound analysis reliability for muscle architecture ( i.e ., FA, FL) were poor to moderate (ICC 2,1 = 0.16–0.72) with moderate absolute error (SEM% = 18.57–31.38).…”

Section: Discussionmentioning

confidence: 99%

“…In the current study, automatic ultrasound analysis reliability for muscle architecture ( i.e ., FA, FL) were poor to moderate (ICC 2,1 = 0.16–0.72) with moderate absolute error (SEM% = 18.57–31.38). Cronin et al (2021) reported reliability statistics of a semi-automated tracing of ultrasound images of the biceps femoris muscle architecture and reported a low coefficient of variation (CV) for the semi-automatic tracing of FA (CV% = 2.58–10.70) and FL (CV% = 0.64–1.12). It is likely that the differences between the previous studies and the present work are due to a difference in muscle architecture ( i.e ., FA, FL) assessment procedures.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, computer programs such as ImageJ (National Institutes of Health) or Photoshop (Adobe) include angle and straight line measurement tools which may be used to calculate FA and FL. Recently, automated analysis tools using various programs ( i.e ., MATLAB, FIJI) used to estimate FA and FL have grown in interest ( Cronin et al, 2021 ; Seynnes & Cronin, 2020 ). Automated tools can be more efficient as less time has to be devoted to training technicians and some automated scripts can bulk-process images instead of the individualized approached required by manual techniques.…”

Section: Introductionmentioning

confidence: 99%

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Reliability and accuracy of ultrasound image analyses completed manually versus an automated tool

Wohlgemuth

Blue

Mota

2022

PeerJ

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Analysis of Brightness-mode ultrasound-captured fascicle angle (FA) and fascicle length (FL) can be completed manually with computer-based programs or by automated programs. Insufficient data exists regarding reliability and accuracy of automated tools. Therefore, the purpose of this study was to determine the test-retest reliability of automatic and manual ultrasound analyses, while determining accuracy of the automatic tool against the manual equivalent. Twenty-three participants (mean ± SD; age = 24 ± 4 years; height = 172.2 ± 10.5 cm; body mass = 73.1 ± 16.1 kg) completed one laboratory visit consisting of two trials where vastus lateralis muscle architecture was assessed with ultrasound. Images were taken at both lower (10 MHz) and higher frequency (12 MHz). Images were analyzed manually in an open-source imaging program and automatically using a separate open-source macro function. Test-retest reliability statistics were calculated for automatic and manual analyses. Accuracy was determined with validity statistics and were calculated for automatic analyses. The results show that manual ultrasound analyses for FA and FL for both lower and higher frequency displayed good reliability (ICC2,1 = 0.75–0.86). However, automatic ultrasound analyses for FA and FL revealed moderate reliability (ICC2,1 = 0.61–0.72) for the lower frequency images and poor reliability (ICC2,1 = 0.16–0.27) for higher frequency images. When assessed against manual techniques, automatic analyses presented greater total error (TE) and standard error of the estimate (SEE) for FA at lower frequency (constant error (CE) = −3.91°, TE = 5.57°, SEE = 3.45°) than higher (CE = −2.78°, TE = −4.54°, SEE = 2.45°). For FL, the higher frequency error (CE = 0.92 cm, TE = 2.12 cm, SEE = 1.15 cm) was similar to lower frequency error (CE = 1.98 cm, TE = 3.66 cm, SEE = 1.57 cm). The findings overall show that manual analyses had good reliability and low absolute error, while demonstrating the automated counterpart had poor to moderate reliability and large errors in analyses. These findings may be impactful as they highlight the good reliability and low error associated with manually analyzed ultrasound images and validate a novel automatic tool for analyzing ultrasound images. Future work should focus on improving reliability and decreasing error in automated image analysis tools. Automated tools are promising for the field as they eliminate biases between analysts and may be more time efficient than manual techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliability and accuracy of ultrasound image analyses completed manually versus an automated tool

Wohlgemuth

Blue

Mota

2022

PeerJ

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“…Recently, machine learning solutions for automatic detection and tracking of musculoskeletal features in biomechanical applications have been developed [28]. These methods improve performance because they can learn to extract salient features, such as anatomical landmarks, directly from annotated input images.…”

Section: Introductionmentioning

confidence: 99%

A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

Leitner,

Jarolim,

Englmair

et al. 2022

Preprint

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Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive dataset, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/.

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“…To date, ultrasound images of muscle ACSA are mostly evaluated manually. However, manual analysis is subjective, laborious, and requires great experience (18,19). Semiautomated and automated algorithms have been developed to accelerate the ACSA segmentation process and reduce the subjectivity of manual segmentation in lower limb muscle ultrasound images (20,21).…”

mentioning

confidence: 99%

DeepACSA: Automatic Segmentation of Cross-Sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning

Ritsche

Wirth²,

Cronin

et al. 2022

Medicine &Amp; Science in Sports &Amp; Exercise

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PurposeMuscle anatomical cross-sectional area (ACSA) can be assessed using ultrasound and images are usually evaluated manually. Here, we present DeepACSA, a deep learning approach to automatically segment ACSA in panoramic ultrasound images of the human rectus femoris (RF), vastus lateralis (VL), gastrocnemius medialis (GM) and lateralis (GL) muscles.MethodsWe trained three muscle-specific convolutional neural networks (CNN) using 1772 ultrasound images from 153 participants (age = 38.2 yr, range = 13–78). Images were acquired in 10% increments from 30% to 70% of femur length for RF and VL and at 30% and 50% of muscle length for GM and GL. During training, CNN performance was evaluated using intersection-over-union scores. We compared the performance of DeepACSA to manual analysis and a semiautomated algorithm using an unseen test set.ResultsComparing DeepACSA analysis of the RF to manual analysis with erroneous predictions removed (3.3%) resulted in intraclass correlation (ICC) of 0.989 (95% confidence interval = 0.983–0.992), mean difference of 0.20 cm2 (0.10–0.30), and SEM of 0.33 cm2 (0.26–0.41). For the VL, ICC was 0.97 (0.96–0.968), mean difference was 0.85 cm2 (−0.4 to 1.31), and SEM was 0.92 cm2 (0.73–1.09) after removal of erroneous predictions (7.7%). After removal of erroneous predictions (12.3%), GM/GL muscles demonstrated an ICC of 0.98 (0.96–0.99), a mean difference of 0.43 cm2 (0.21–0.65), and an SEM of 0.41 cm2 (0.29–0.51). Analysis duration was 4.0 ± 0.43 s (mean ± SD) for analysis of one image in our test set using DeepACSA.ConclusionsDeepACSA provides fast and objective segmentation of lower limb panoramic ultrasound images comparable with manual segmentation. Inaccurate model predictions occurred predominantly on low-quality images, highlighting the importance of high-quality image for accurate prediction.

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Semi-automated Tracing of Hamstring Muscle Architecture for B-mode Ultrasound Images

Cited by 7 publications

References 30 publications

Reliability and accuracy of ultrasound image analyses completed manually versus an automated tool

Reliability and accuracy of ultrasound image analyses completed manually versus an automated tool

A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

DeepACSA: Automatic Segmentation of Cross-Sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning

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