Abstract
In multidisciplinary computational anatomy (MCA), its scheme will be expanded in spatial, time series, functional, and pathological axes. Therefore, we have expected computer-aided diagnosis (CAD) applications based on this scheme are able to support diagnosis for wide range of clinical images including not only radiological images, but also pathological images and autopsy images. From these axes of views, we have developed robust CAD methods for pathological lungs such as diffuse lung diseases (DLD), lung nodules, and also colon polyps. In addition, we have obtained three dimensional (3D)-scanned images of whole lungs as new pathological images to assist diagnosis of clinical images.
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References
Mabu S, Obayashi M, Kuremoto T, Hashimoto N, Hirano Y, Kido S. Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns. Int J Comput Assist Radiol Surg. 2017;12(3):519–28.
Rui X, Hirano Y, Tachibana R, Kido S. A bag-of-features approach to classify six types of pulmonary textures on high-resolution computed tomography. IEICE Trans Inf Syst. 2013;96(4):845–55.
Mabu S, Kido S, Hirano Y, Kuremoto T. Unsupervised and semi-supervised learning for efficient opacity annotation of diffuse lung diseases. In: SPIE 11050, International Forum on Medical Imaging in Asia; 2019. p. 110501D-1–6.
Mabu S, Atsumo A, Kido S, Kuremoto T, Hirano Y. Investigating the effects of transfer learning on ROI-based classification of chest CT images: a case study on diffuse lung diseases. J Signal Process Syst. 2020;92(3):307–13.
CIFAR-10 and CIFAR-100 datasets. Accessed: 15 July 2017 https://www.cs.toronto.edu/~kriz/cifar.html
Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. British J Rad. 2005;78:S3–S19.
Lu K, Li T, Kim M, Aoki K. Extraction of GGO candidate regions on thoracic CT images using Supervoxel-based graph cuts for healthcare systems. Mobile Networks and Applications. 2018;23(6):1669–79.
Itai K, Ishikawa K, Doi. Development of a voxel matching technique for substantial reduction of subtraction artifacts in temporal subtraction images obtained from thoracic MDCT. J Digit Imaging. 2010;23(1):31–8.
Yoshino M, Tan L, Kim M, Aoki T, Hirano K. Automatic classification of lung nodules on MDCT images with the temporal subtraction technique. Int J Comput Assist Radiol Surg. 2017;12(10):1789–98.
Hirayama M, Lu T, Kim T, Hirano K. Extracting of GGO regions from chest CT images using deep learning. In: Proceedings of the 17th International Conference on Control, Automation and Systems; 2017. p. 351–5.
Lung Image Database Consortium (LIDC) – Cancer Imaging Program http://imaging.cancer.gov/programsandresources/informationsystems/lidc
Kushner HJ. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J Fluids Eng. 1964;86(1):97–106.
Hirano Y, Ito T, Kido S et al. Automated construction of the optimal structure for 3D CNN by Using the Bayesian Optimization. In: RSNA 2018. Radiological Society of North America, Chicago; 2018.
Tachibana R, Näppi JJ, Ota J, Kohlhase N, Hironaka T, Kim SH, Regge D, Yoshida H. Deep learning electronic cleansing for single- and dual-energy CT colonography. Radiographics. 2018;38:2034–50.
Tachibana R, Näppi J J, Yoshida H. The next step in electronic cleansing for CT colonography: unsupervised machine learning. Radiological Society of North America 2018 Scientific Assembly and Annual Meeting, Chicago; 2018.
Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. p. 1125–1134.
Tachibana R, Näppi J, Hironaka T, Yoshida H. Electronic cleansing in CT colonography using a generative adversarial network. Proc SPIE Med Imaging. 2019;10954:1095419.
Inai K, Noriki S, Kinoshita K, et al. Postmortem CT is more accurate than clinical diagnosis for identifying the immediate cause of death in hospitalized patients: a prospective autopsy-based study. Virchows Arch. 2016;469:101–9.
Ichikado K, Johkoh T, Ikezoe J, et al. Acute interstitial pneumonia: high-resolution CT findings correlated with pathology. Am J Roentgenol. 1997;168:333–8.
Tozawa K, Saito A, Kido S, et al. Super resolution of a lung CT volume using a generative adversarial network. In: Computer assisted radiology and surgery, 32th international congress and exhibition (CARS2018). Berlin: Int J Comput Assist Radiol Surg; 2018. p. 170–1.
Acknowledgements
This research was supported by research grants of Grant-in-Aid for Scientific Research on Innovative areas, MEXT, Japanese Government.
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Kido, S., Mabu, S., Kamiya, T., Hirano, Y., Tachibana, R., Inai, K. (2022). Clinical Applications of MCA to Diagnosis. In: Hashizume, M. (eds) Multidisciplinary Computational Anatomy. Springer, Singapore. https://doi.org/10.1007/978-981-16-4325-5_10
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DOI: https://doi.org/10.1007/978-981-16-4325-5_10
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