Segmentation label propagation using deep convolutional neural networks and dense conditional random field
Abstract
Availability and accessibility of large-scale annotated medical image datasets play an essential role in robust supervised learning of medical image analysis. Missed labeling of regions of interest is a common issue on existing medical image datasets due to the labor intensive nature of the annotation task which requires high levels of clinical proficiency. In this paper, we present a segmentation based label propagation method to a publicly available dataset on interstitial lung disease [3], to address the missing annotation challenge. Upon validation from an expert radiologist, the amount of available annotated training data is largely increased. Such a dataset expansion can can potentially increase the accuracy of Computer-aided Detection (CAD) systems. The proposed constrained segmentation propagation algorithm combines the cues from the initial annotations, deep convolutional neural networks and a dense fully-connected Conditional Random Field (CRF) that achieves high quantitative accuracy levels.
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A segmentation label propagation method was recently proposed to provide full pixel-wise labeling of the ILD data images.
“A segmentation label propagation method was recently proposed [47] to provide full pixel-wise labeling of the ILD data images.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [47]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Building a reference multimedia database for interstitial lung diseases
Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.-A., & Müller, H. (2012) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2011.07.003 · Cited by 248 · cited 2× · see citing works
“2016 International Symposium on Biomedical Imaging References: [1] Depeursinge, Adrien, et al.”
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works
“A segmentation label propagation method was recently proposed [47] to provide full pixel-wise labeling of the ILD”
BackgroundMethodSelf-citationEmbracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095 · see citing works
- Authors
- Gao, M., Xu, Z., Lu, L., Wu, A., Nogues, I., Summers, R. M., & Mollura, D. J.
- Venue
- 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2016-04
- Pages
- 1265-1268
- OpenAlex
- W2444070604
- Cited by
- 48
- References
- 2
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 2 · 2022: 5 · 2021: 7 · 2020: 6 · 2019: 11 · 2018: 2 · 2017: 7 · 2016: 2
- FWCI
- 2.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Funders
- Nvidia
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- label propagation, medical image segmentation, interstitial lung disease, missing annotations, dataset expansion, conditional random fields, deep convolutional neural networks, computer-aided detection
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