| Paper Abstract and Keywords |
| Presentation |
2022-05-20 16:40
3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN
Yuya Okumura, Kudo Hiroyuki, Takizawa Hotaka (Tsukuba Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
An effective method to improve the accuracy of 3D medical image segmentation using deep learning is to use deformable convolutional CNN, which can absorb individual differences in organ structure and misalignment using displacement vector fields. However, the natural extension from 2D to 3D is impractical due to the huge amount of computation required to calculate and store the displacement vector fields. In this study, we propose a 2.5D method to solve this problem, in which a deformable convolutional CNN is used to perform segmentation in 2D cross sections of xy, yz, and xz horizontal sections, and the results are integrated by majority voting to obtain 3D segmentation results. Experimental results on a real CT image dataset of the abdomen show that the proposed method is more accurate than conventional deep learning methods due to the introduction of deformable convolution, and the computational complexity of the proposed method is realistic for a 2.5D method. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
CT images / Deep learning / Convolutional Neural Networks / 3D CT images / Computer-aided Detection Systems / Automatic recognition and detection of anatomical structures / / |
| Reference Info. |
ITE Tech. Rep. |
| Paper # |
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| Download PDF |
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| Conference Information |
| Committee |
IEICE-SIP IEICE-BioX IEICE-IE IEICE-MI IST ME |
| Conference Date |
2022-05-19 - 2022-05-20 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Kumamoto University |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
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| Paper Information |
| Registration To |
IEICE-MI |
| Conference Code |
2022-05-SIP-BioX-IE-MI-IST-ME |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN |
| Sub Title (in English) |
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| Keyword(1) |
CT images |
| Keyword(2) |
Deep learning |
| Keyword(3) |
Convolutional Neural Networks |
| Keyword(4) |
3D CT images |
| Keyword(5) |
Computer-aided Detection Systems |
| Keyword(6) |
Automatic recognition and detection of anatomical structures |
| Keyword(7) |
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| Keyword(8) |
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| 1st Author's Name |
Yuya Okumura |
| 1st Author's Affiliation |
University of Tsukuba (Tsukuba Univ.) |
| 2nd Author's Name |
Kudo Hiroyuki |
| 2nd Author's Affiliation |
University of Tsukuba (Tsukuba Univ.) |
| 3rd Author's Name |
Takizawa Hotaka |
| 3rd Author's Affiliation |
University of Tsukuba (Tsukuba Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2022-05-20 16:40:00 |
| Presentation Time |
20 minutes |
| Registration for |
IEICE-MI |
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| Volume (vol) |
vol.46 |
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