Intervertebral disc detection in X-ray images using faster R-CNN
Abstract
Automatic identification of specific osseous landmarks on the spinal radiograph can be used to automate calculations for correcting ligament instability and injury, which affect 75% of patients injured in motor vehicle accidents. In this work, we propose to use deep learning based object detection method as the first step towards identifying landmark points in lateral lumbar X-ray images. The significant breakthrough of deep learning technology has made it a prevailing choice for perception based applications, however, the lack of large annotated training dataset has brought challenges to utilizing the technology in medical image processing field. In this work, we propose to fine tune a deep network, Faster-RCNN, a state-of-the-art deep detection network in natural image domain, using small annotated clinical datasets. In the experiment we show that, by using only 81 lateral lumbar X-Ray training images, one can achieve much better performance compared to traditional sliding window detection method on hand crafted features. Furthermore, we fine-tuned the network using 974 training images and tested on 108 images, which achieved average precision of 0.905 with average computation time of 3 second per image, which greatly outperformed traditional methods in terms of accuracy and efficiency.
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Deep learning is increasingly used in medical imaging for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.
“In medical imaging, deep learning is increasingly used for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [12]
Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error.
“Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error [9–17].”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [12]
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Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554 · see citing works
Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views
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“detected intervertebral discs in X-ray images of the spine to predict a bounding box of the respective vertebrae, by using a pretrained Faster-RNN and refining its weights [21].”
Deep Learning Strategies for Ultrasound in Pregnancy
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- Authors
- Sa, R., Owens, W., Wiegand, R., Studin, M., Capoferri, D., Barooha, K., Greaux, A., Rattray, R., Hutton, A., Cintineo, J., & Chaudhary, V.
- Affiliations
- University at Buffalo, State University of New York (US)
- University of Bridgeport (US)
- ORCID
- Chaudhary, Vipin
- Venue
- 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2017-07
- Volume
- 2017
- Pages
- 564-567
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2754210552
- Cited by
- 90
- References
- 13
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 14 · 2023: 5 · 2022: 16 · 2021: 17 · 2020: 15 · 2019: 9 · 2018: 8
- FWCI
- 17.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- Faster R-CNN, object detection, spinal radiographs, ligament instability, fine-tuning, small annotated datasets
- MeSH terms
- Humans, Image Processing, Computer-Assisted, Intervertebral Disc, Learning, X-Rays, Neural Networks, Computer
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