Web-Based Spine Segmentation Using Deep Learning in Computed Tomography Images
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초록
Objectives: Back pain, especially lower back pain, is experienced in 60% to 80% of adults at some points during their lives. Various studies have found that lower back pain is a very common problem among adolescents, and the highest incidence rates are for adults in their 30s. There has been a remarkable increase in using computer-aided diagnosis to assist doctors in the interpretation of medical images. Spine segmentation in computed tomography (CT) scans using algorithmic methods allows improved diagnosis of back pain. Methods: In this study, we developed a web-based automatic spine segmentation method using deep learning and obtained the dice coefficient by comparison with the predicted image. Our method is based on convolutional neural networks for segmentation. More specifically, we train a hierarchical data format file using U-Net architecture and then insert the test data label to perform segmentation. Thus, we obtained more specific and detailed results. A total of 344 CT images were used in the experiment. Of these, 330 were used for learning, and the remaining 14 for testing. Results: Our method achieved an average dice coefficient of 90.4%, a precision of 96.81%, and an F1-score of 91.64%. Conclusions: The proposed web-based deep learning approach can be very practical and accurate for spine segmentation as a diagnostic method.
Abstract
Objectives: Back pain, especially lower back pain, is experienced in 60% to 80% of adults at some points during their lives. Various studies have found that lower back pain is a very common problem among adolescents, and the highest incidence rates are for adults in their 30s. There has been a remarkable increase in using computer-aided diagnosis to assist doctors in the interpretation of medical images. Spine segmentation in computed tomography (CT) scans using algorithmic methods allows improved diagnosis of back pain. Methods: In this study, we developed a web-based automatic spine segmentation method using deep learning and obtained the dice coefficient by comparison with the predicted image. Our method is based on convolutional neural networks for segmentation. More specifically, we train a hierarchical data format file using U-Net architecture and then insert the test data label to perform segmentation. Thus, we obtained more specific and detailed results. A total of 344 CT images were used in the experiment. Of these, 330 were used for learning, and the remaining 14 for testing. Results: Our method achieved an average dice coefficient of 90.4%, a precision of 96.81%, and an F1-score of 91.64%. Conclusions: The proposed web-based deep learning approach can be very practical and accurate for spine segmentation as a diagnostic method.
참고문헌 (17)
- "Back pain"[URL]
- Abu-Naser SS, "Lower back pain expert system diagnosis and treatment", J Multidiscip Eng Sci Stud, 2(4), pp.441~446 (2016)
- National Academies of Sciences, "Engineering, and Medicine. Improving diagnosis in health care" (2015), National Academies Press
- Shiraishi J, "Computer-aided diagnosis and artificial intelligence in clinical imaging", Semin Nucl Med, 41(6), pp.449~462 (2011)
- "Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling" (2017)[URL]
- Kamboj A, "Simulink model based image segmentation", Int J Adv Res Comput Sci Softw Eng, 2(6), pp.146~149 (2012)
- Lathen G., "Segmentation methods for medical image analysis: blood vessels, multi-scale filtering and level set methods", Master, pp.2010
- Nyul LG, "Computer analysis of images and patterns", pp.456~463 (2005), Springer
- Folk M, "An overview of the HDF5 technology suite and its applications", Proceedings of the EDBT, 2011, 36~47
- Collette A, "Python and HDF5: unlocking scientific data" (2013), O'Reilly Media Inc
- Ronneberger O, "Medical image computing and computer-assisted intervention –MICCAI 2015", pp.234~241 (2015), Springer
- Dong H, "Medical image understanding and analysis", pp.506~517 (2017), Springer
- Grinberg M, "Flask web development: developing web applications with python" (2018), O'Reilly Media Inc
- Mainland G, "Sensor network programming with Flask", Proceedings of the 5th International Conference on Embedded Networked Sensor Systems, pp.385~386 (2017)
- Van der Walt S, "The NumPy array : a structure for efficient numerical computation", Comput Sci Eng, 13(2), pp.22~30 (2011)
- Zou KH, "Statistical validation of image segmentation quality based on a spatial overlap index1 : scientific reports", Acad Radiol, 11(2), pp.178~189 (2004)
- Davis J, "The relationship between precision-recall and ROC curves", Proceedings of the 23rd International Conference on Machine Learning, pp.233~240 (2006)