Examining the Validity of Input Lung CT Images Submitted to the AI-Based Computerized Diagnosis

Aleksandra A. Kosareva (Login required)
Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus

Dzmitry A. Paulenka
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus

Eduard V. Snezhko
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus

Ivan A. Bratchenko
Samara University, Department of Laser and Biotechnical Systems, Samara, Russia

Vassili A. Kovalev
The United Institute of Informatics Problems of NAS of Belarus (UIIP NASB), Biomedical Image Analysis Department, Minsk, Belarus


Paper #3500 received 23 Jun 2022; revised manuscript received 05 Sep 2022; accepted for publication 16 Sep 2022; published online 30 Sep 2022.

DOI: 10.18287/JBPE22.08.030307

Abstract

A well-designed CAD tool should respond to input requests, user actions, and perform input checks. Thus, an important element of such a tool is the pre-processing of incoming data and screening out those data that cannot be processed by the application. In this paper, we consider non-trivial methods of chest computed tomography (CT) images verifications: modality and human chest checks. We review sources to develop training datasets, describe architectures of convolution neural networks (CNN), clarify pre-processing and augmentation processes of chest CT scans and show results of training. The developed application showed good results: 100% classification accuracy on the test dataset for modality check and 89% classification accuracy on the test dataset for checking of lungs presence. Analysis of wrong predictions showed that the model performs poorly on biopsy of lungs. In general, the developed input data validation model shows good results on the designed datasets for CT image modality check and for checking of lungs presence.

Keywords

image classification; medical imaging; convolutional neural network; deep learning; computer-aided diagnosis; computed tomography; input validation

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References


1. A. Rosenthal, A. Gabrielian, E. Engle, et al., “The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis,” Journal of Clinical Microbiology 55(11), 3267–3282 (2017).

2. S. Kaplan, D. Handelman, and A. Handelman, “Sensitivity of neural networks to corruption of image classification,” AI Ethics 1, 425–434 (2021).

3. D. Guan, W. Yuan, Y.-K. Lee, and S. Lee, “Identifying mislabeled training data with the aid of unlabeled data,” Applied Intelligence 35(3), 345–358 (2011).

4. A. P. Brady, “Error and discrepancy in radiology: inevitable or avoidable?” Insights Imaging 8(1), 171–182 (2017).

5. K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, and F. Prior, “The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository,” Journal of Digital Imaging 26(6), 1045–1057 (2013).

6. J. P. Cohen, P. Morrison, and L. Dao, “COVID-19 Image Data Collection,” arXiv:2003.11597v1 (2020).

7. J. Yang, G. Sharp, H. Veeraraghavan, W. Van Elmpt, A. Dekker, T. Lustberg, and M. Gooding, “Data from Lung CT Segmentation Challenge,” The Cancer Imaging Archive (2017).

8. S. G. Armato, G. McLennan, L. Bidaut, et al., “The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans: The LIDC/IDRI thoracic CT database of lung nodules,” Medical Physics 38(2), 915–931 (2011).

9. H. Roth, A. Farag, E. B. Turkbey, L. Lu, J. Liu, and R. M. Summers, “Data From Pancreas-CT,” The Cancer Imaging Archive (2016).

10. K. Yan, X. Wang, L. Lu, and R. M. Summers, “DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,” Journal of Medical Imaging 5(3), 036501 (2018).

11. M. Vallières, E. Kay-Rivest, L. Perrin, X. Liem, C. Furstoss, N. Khaouam, P. Nguyen-Tan, C.-S. Wang, and K. Sultanem, “Data from Head-Neck-PET-CT,” The Cancer Imaging Archive (2017).

12. P. Kinahan, M. Muzi, B. Bialecki, B. Herman, and L. Coombs, “Data from the ACRIN 6668 Trial NSCLC-FDG-PET,” The Cancer Imaging Archive (2019).

13. M. Patnana, S. Patel, and A. S. Tsao, “Data from Anti-PD-1 Immunotherapy Lung,” The Cancer Imaging Archive (2019).

14. J. Eisenbrey, A. Lyshchik, and C. Wessner, “Ultrasound data of a variety of liver masses,” The Cancer Imaging Archive (2021).

15. S. Natarajan, A. Priester, D. Margolis, J. Huang, and L. Marks, “Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy),” The Cancer Imaging Archive (2020).

16. P. J. LaMontagne, T. LS. Benzinger, J. C. Morris, S. Keefe, R. Hornbeck, C. Xiong, E. Grant, J. Hassenstab, K. Moulder, A. G. Vlassenko, M. E. Raichle, C. Cruchaga, and D. Marcus, “OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease,” medRxiv 2019.12.13.19014902 (2019).

17. D. E. Job, D. A. Dickie, D. Rodriguez, A. Robson, S. Danso, C. Pernet, M. E. Bastin, J. P. Boardman, A. D. Murray, T. Ahearn, G. D. Waiter, R. T. Staff, I. J. Deary, S. D. Shenkin, and J. M. Wardlaw, “A brain imaging repository of normal structural MRI across the life course: Brain Images of Normal Subjects (BRAINS),” NeuroImage 144, 299–304 (2017).

18. D. Newitt, N. Hylton, “Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy,” The Cancer Imaging Archive (2016).

19. K. Jafari-Khouzani, K. Elisevich, S. Patel, and H. Soltanian-Zadeh, “Dataset of magnetic resonance images of nonepileptic subjects and temporal lobe epilepsy patients for validation of hippocampal segmentation techniques,” Neuroinformatics 9, 335–346 (2011).

20. National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC), “The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA),” The Cancer Imaging Archive (2018).

21. A. Badano, C. G. Graff, A. Badal, D. Sharma, R. Zeng, F. W. Samuelson, S. Glick, and K. J. Myers, “Data from the VICTRE trial: open-source, in-silico clinical trial for evaluating digital breast tomosynthesis (VICTRE),” The Cancer Imaging Archive (2019).

22. M.-P. Revel, S. Boussouar, C. de Margerie-Mellon, I. Saab, T. Lapotre, D. Mompoint, G. Chassagnon, A. Milon, M. Lederlin, S. Bennani, S. Molière, M.-P. Debray, F. Bompard, S. Dangeard, C. Hani, M. Ohana, S. Bommart, C. Jalaber, M. El Hajjam, I. Petit, L. Fournier, A. Khalil, P.-Y. Brillet, M.-F. Bellin, A. Redheuil, L. Rocher, V. Bousson, P. Rousset, J. Grégory, J.-F. Deux, E. Dion, D. Valeyre, R. Porcher, L. Jilet, and H. Abdoul, “Study of Thoracic CT in COVID-19: The STOIC Project,” Radiology 301(1), E361–E370 (2021).

23. S. P. Morozov, A. E. Andreychenko, N. A. Pavlov, A. V. Vladzymyrskyy, N. V. Ledikhova, V. A. Gombolevskiy, I. A. Blokhin, P. B. Gelezhe, A. V. Gonchar, and V. Yu. Chernina, “MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset,” arXiv:2005.06465v1 (2020).

24. L. Jin, J. Yang, K. Kuang, B. Ni, Y. Gao, Y. Sun, P. Gao, W. Ma, M. Tan, H. Kang, J. Chen, and M. Li, “Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet,” eBioMedicine 62, 103106 (2020).

25. S. Chilamkurthy, R. Ghosh, S. Tanamala, M. Biviji, N. G. Campeau, V. K. Venugopal, V. Mahajan, P. Rao, and P. Warier, “Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study,” The Lancet 392(10162), 2388–2396 (2018).

26. E. Sogancioglu, K. Murphy, and B. Van Ginneken, “NODE21,” Zenodo (2021).

27. H. Zunair, A. Rahman, N. Mohammed, and J. P. Cohen, “Uniformizing Techniques to Process CT Scans with 3D CNNs for Tuberculosis Prediction,” in Predictive Intelligence in Medicine, I. Rekik, E. Adeli, S. H. Park, and M. del C. Valdés Hernández (Eds. ), Springer International Publishing 12329, Cham, 156–168 (2020).






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