A1 Journal article (refereed)
DeepACSA : Automatic Segmentation of Cross-sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning (2022)


Ritsche, P., Wirth, P., Cronin, N. J., Sarto, F., Narici, M. V., Faude, O., & Franchi, M. V. (2022). DeepACSA : Automatic Segmentation of Cross-sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning. Medicine and Science in Sports and Exercise, 54(12), 2188-2195. https://doi.org/10.1249/MSS.0000000000003010


JYU authors or editors


Publication details

All authors or editorsRitsche, Paul; Wirth, Philipp; Cronin, Neil J.; Sarto, Fabio; Narici, Marco V.; Faude, Oliver; Franchi, Martino V.

Journal or seriesMedicine and Science in Sports and Exercise

ISSN0195-9131

eISSN1530-0315

Publication year2022

Publication date05/08/2022

Volume54

Issue number12

Pages range2188-2195

PublisherLippincott Williams & Wilkins

Publication countryUnited Kingdom

Publication languageEnglish

DOIhttps://doi.org/10.1249/MSS.0000000000003010

Publication open accessNot open

Publication channel open access

Web address of parallel published publication (pre-print)https://doi.org/10.1101/2021.12.27.21268258


Abstract

Purpose
Muscle 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.

Methods
We trained three muscle-specific convolutional neural networks (CNNs) using 1772 ultrasound images from 153 participants (age = 38.2 years, 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 semi-automated algorithm using an unseen test set.

Results
Comparing DeepACSA analysis of the RF to manual analysis with erroneous predictions removed (3.3%) resulted in intra-class correlation (ICC) of 0.989 (95% CI 0.983;0.992), mean difference of 0.20 cm2 (0.10;0.30) and standard error of the differences (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 (-0.4,1.31) and SEM was 0.92 cm2 (0.73,1.09) following 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 a SEM of 0.41 cm2 (0.29,0.51). Analysis duration was 4.0 s standard deviation (SD) ± 0.43 for analysis of one image in our test set using DeepACSA.

Conclusions
DeepACSA provides fast and objective segmentation of lower limb panoramic ultrasound images comparable to manual segmentation. Inaccurate model predictions occurred predominantly on low-quality images, highlighting the importance of high image quality for accurate prediction.


Keywordsmusclesultrasonographyimagingsegmentationmachine learningdeep learningneural networks (information technology)


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating3


Last updated on 2024-26-03 at 09:21