A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Ritsche, Paul; Wirth, Philipp; Cronin, Neil J.; Sarto, Fabio; Narici, Marco V.; Faude, Oliver; Franchi, Martino V.
Lehti tai sarja: Medicine and Science in Sports and Exercise
ISSN: 0195-9131
eISSN: 1530-0315
Julkaisuvuosi: 2022
Ilmestymispäivä: 05.08.2022
Volyymi: 54
Lehden numero: 12
Artikkelin sivunumerot: 2188-2195
Kustantaja: Lippincott Williams & Wilkins
Julkaisumaa: Britannia
Julkaisun kieli: englanti
DOI: https://doi.org/10.1249/MSS.0000000000003010
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Rinnakkaistallenteen verkko-osoite (pre-print): https://doi.org/10.1101/2021.12.27.21268258
Tiivistelmä
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.
YSO-asiasanat: lihakset; ultraäänitutkimus; kuvantaminen; segmentointi; koneoppiminen; syväoppiminen; neuroverkot
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2022
JUFO-taso: 3
