Using Convolutional Neural Network for edge detection in musculoskeletal ultrasound images

Shaima I. Jabbar, Charles R. Day, Nicholas Heinz, Edward K. Chadwick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherIEEE Explore
Pages4619-4626
Number of pages8
ISBN (Electronic)9781509006205, 9781509006199
ISBN (Print)9781509006199, 9781509006212
DOIs
Publication statusPublished - 31 Oct 2016
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October
ISSN (Electronic)2161-4407

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • Convolutional Neural Networks
  • Musculoskeletal model
  • Segmentation
  • Ultrasound image

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  • Cite this

    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 2016 (pp. 4619-4626). [7727805] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2016-October). IEEE Explore. https://doi.org/10.1109/IJCNN.2016.7727805