| Presentation | 2021-07-09 Severity determination of chest CT data in tuberculosis patients using deep learning Tetsuya Asakawa, Riku Tsuneda, Kazuki Simizu, Takuyuki Komoda, Masaki Aono, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The purpose of this study is to make accurate estimates for five labels (infiltrative, focal, tuberculoma, miliary, and fi- brocavernous) based on lung images. We describe the tuberculosis task and approach for chest CT image analysis and then perform a single- label CT image analysis using the task dataset. We propose an image processing and fine-tuning deep neural network model that uses inputs from convolutional neural network features. This paper presents several approaches for applying normalization and pseudo-color to the extracted 2D images, for applying mask data to the extracted 2D image data, and for extracting a set of 2D projection images based on the 3D chest CT data. Our submissions for the task test dataset achieved an unweighted Cohen’s kappa of 0.236 and an accuracy of 0.471. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Computed Tomography / Tuberculosis / Deep Learning / Normalization / Pseudo-color |
| Paper # | MI2021-19 |
| Date of Issue | 2021-07-01 (MI) |
| Conference Information | |
| Committee | MI |
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| Conference Date | 2021/7/8(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical imaging, physics, and recognition |
| Chair | Hidekata Hontani(Nagoya Inst. of Tech.) |
| Vice Chair | Hideaki Haneishi(Chiba Univ.) / Takayuki Kitasaka(Aichi Inst. of Tech.) |
| Secretary | Hideaki Haneishi(Yamaguchi Univ.) / Takayuki Kitasaka(Univ. of Hyogo) |
| Assistant | Hotaka Takizawa(Tsukuba Univ.) / Yoshito Otake(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Medical Imaging |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Severity determination of chest CT data in tuberculosis patients using deep learning |
| Sub Title (in English) | |
| Keyword(1) | Computed Tomography |
| Keyword(2) | Tuberculosis |
| Keyword(3) | Deep Learning |
| Keyword(4) | Normalization |
| Keyword(5) | Pseudo-color |
| 1st Author's Name | Tetsuya Asakawa |
| 1st Author's Affiliation | Toyohashi University of Technology(TUT) |
| 2nd Author's Name | Riku Tsuneda |
| 2nd Author's Affiliation | Toyohashi University of Technology(TUT) |
| 3rd Author's Name | Kazuki Simizu |
| 3rd Author's Affiliation | Toyohashi Heart Center(THC) |
| 4th Author's Name | Takuyuki Komoda |
| 4th Author's Affiliation | Toyohashi Heart Center(THC) |
| 5th Author's Name | Masaki Aono |
| 5th Author's Affiliation | Toyohashi University of Technology(TUT) |
| Date | 2021-07-09 |
| Paper # | MI2021-19 |
| Volume (vol) | vol.121 |
| Number (no) | MI-98 |
| Page | pp.pp.42-46(MI), |
| #Pages | 5 |
| Date of Issue | 2021-07-01 (MI) |