| Presentation | 2022-05-20 3D Medical Image Segmentation Using 2.5D Deformable Convolutional CNN Yuya Okumura, Kudo Hiroyuki, Takizawa Hotaka, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | CT images / Deep learning / Convolutional Neural Networks / 3D CT images / Computer-aided Detection Systems / Automatic recognition and detection of anatomical structures |
| Paper # | SIP2022-29,BioX2022-29,IE2022-29,MI2022-29 |
| Date of Issue | 2022-05-12 (SIP, BioX, IE, MI) |
| Conference Information | |
| Committee | SIP / BioX / IE / MI / ITE-IST / ITE-ME |
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| Conference Date | 2022/5/19(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kumamoto University Kurokami Campus |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Yukihiro Bandou(NTT) / Hitoshi Imaoka(NEC) / Kazuya Kodama(NII) / Hidekata Hontani(Nagoya Inst. of Tech.) |
| Vice Chair | Toshihisa Tanaka(Tokyo Univ. Agri.&Tech.) / Takayuki Nakachi(Ryukyu Univ.) / Masatsugu Ichino(Univ. of Electro-Comm.) / Naoyuki Takada(SECOM) / Hiroyuki Bandoh(NTT) / Toshihiko Yamazaki(Univ. of Tokyo) / Hideaki Haneishi(Chiba Univ.) / Takayuki Kitasaka(Aichi Inst. of Tech.) |
| Secretary | Toshihisa Tanaka(Xiaomi) / Takayuki Nakachi(Takushoku Univ.) / Masatsugu Ichino(Tokyo Univ. Agri.&Tech.) / Naoyuki Takada(KDDI Research) / Hiroyuki Bandoh(MitsubishiElectric) / Toshihiko Yamazaki(KDDI Research) / Hideaki Haneishi(Nagoya Inst. of Tech.) / Takayuki Kitasaka(Yamaguchi Univ.) / (Univ. of Hyogo) |
| Assistant | Taichi Yoshida(UEC) / Seisuke Kyochi(Univ. of Kitakyushu) / Hiroyuki Suzuki(Gunma Univ) / Akihiro Hayasaka(NEC) / Shunsuke Iwamura(NHK) / Shinobu Kudo(NTT) / Hotaka Takizawa(Tsukuba Univ.) / Yoshito Otake(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Signal Processing / Technical Committee on Biometrics / Technical Committee on Image Engineering / Technical Committee on Medical Imaging / Technical Group on Information Sensing Technologies / Technical Group on Media Engineering |
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| Language | JPN |
| 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) | |
| 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 |
| 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.) |
| Date | 2022-05-20 |
| Paper # | SIP2022-29,BioX2022-29,IE2022-29,MI2022-29 |
| Volume (vol) | vol.122 |
| Number (no) | SIP-28,BioX-29,IE-30,MI-31 |
| Page | pp.pp.150-155(SIP), pp.150-155(BioX), pp.150-155(IE), pp.150-155(MI), |
| #Pages | 6 |
| Date of Issue | 2022-05-12 (SIP, BioX, IE, MI) |