Shaima I. Jabbar
Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images
Jabbar, Shaima I.; Day, Charles R.; Heinz, Nicholas; Chadwick, Edward K.
Abstract
Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging approaches; secondly, the sometimes subtle interaction between musculoskeletal components that leads to non-uniformity of the image intensity. Our work presents an investigation of the potential of Convolutional Neural Networks (CNNs) to address both of these problems. CNNs are an effective tool that has previously been used in image processing of several biomedical imaging modalities. However, there is little published material addressing the processing of musculoskeletal ultrasound images. In our work we explore the effectiveness of CNNs when trained to act as a pre-segmentation pixel classifier that determines whether a pixel is an edge or non-edge pixel. Our CNNs are trained using two different ground truth interpretations. The first one uses an automatic Canny edge detector to provide the ground truth image; the second ground truth was obtained using the same image marked-up by an expert anatomist. In this initial study the CNNs have been trained using half of the prepared data from one image, using the other half for testing; validation was also carried out using three unseen ultrasound images. CNN performance was assessed using Mathew's Correlation Coefficient, Sensitivity, Specificity and Accuracy. The results show that CNN performance when using expert ground truth image is better than using Canny ground truth image. Our technique is promising and has the potential to speed-up the image processing pipeline using appropriately trained CNNs.
Citation
Jabbar, S. I., Day, C. R., Heinz, N., & Chadwick, E. K. (2016). Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images. In 2016 International Joint Conference on Neural Networks (IJCNN)
Conference Name | International Joint Conference on Neural Networks |
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Conference Location | Vancouver |
Start Date | Jul 25, 2016 |
End Date | Jul 29, 2016 |
Acceptance Date | Mar 15, 2016 |
Online Publication Date | Nov 3, 2016 |
Publication Date | Nov 3, 2016 |
Publicly Available Date | May 26, 2023 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Series Title | International Joint Conference on Neural Networks 2016 |
Book Title | 2016 International Joint Conference on Neural Networks (IJCNN) |
ISBN | 978-1-5090-0619-9 |
Keywords | Segmentation, Convolutional Neural Networks, Musculoskeletal model, Ultrasound image |
Publisher URL | http://dx.doi.org/10.1109/IJCNN.2016.7727805 |
Files
IJCNN2016-PAPER JABBAR.pdf
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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