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Automatic Tracking of Muscle CrossâSectional Area Using Convolutional Neural Networks with Ultrasound
- Source :
- Journal of Ultrasound in Medicine. 38:2901-2908
- Publication Year :
- 2019
- Publisher :
- Wiley, 2019.
-
Abstract
- Objectives The purpose of this study was to develop an automatic tracking method for the muscle cross-sectional area (CSA) on ultrasound (US) images using a convolutional neural network (CNN). The performance of the proposed method was evaluated and compared with that of the state-of-the art muscle segmentation method. Methods A real-time US image sequence was obtained from the rectus femoris muscle during voluntary contraction. A CNN was built to segment the rectus femoris muscle and calculate the CSA in each US frame. This network consisted of 2 stages: feature extraction and score map reconstruction. The training of the network was divided into 3 steps with output score map resolutions of one-fourth, one-half, and all of the original image. We evaluated the segmentation performance of our method with 5-fold cross-validation. The mean precision, recall, and dice similarity score were calculated. Results The mean precision, recall, and Dice's coefficient (DSC) ± SD were 0.936 ± 0.029, 0.882 ± 0.045, and 0.907 ± 0.023, respectively. Compared with the state-of-the-art muscle segmentation method (constrained mutual-information-based free-form deformation), the proposed method using CNN showed high performance. Conclusions The automated method proposed in this study provides an accurate and efficient approach to the estimation of the muscle CSA during muscle contraction.
- Subjects :
- Adult
Male
Feature extraction
Rectus femoris muscle
Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Similarity (network science)
Reference Values
Image Processing, Computer-Assisted
medicine
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Muscle, Skeletal
Ultrasonography
030219 obstetrics & reproductive medicine
Radiological and Ultrasound Technology
business.industry
Deep learning
Ultrasound
Pattern recognition
Female
Neural Networks, Computer
Artificial intelligence
medicine.symptom
business
Muscle contraction
Subjects
Details
- ISSN :
- 15509613 and 02784297
- Volume :
- 38
- Database :
- OpenAIRE
- Journal :
- Journal of Ultrasound in Medicine
- Accession number :
- edsair.doi.dedup.....f6d00fb9b772d7b07d7d4501fe0231eb