| Presentation | 2022-07-08 [Short Paper] Multi-phase CT Image Segmentation with Single-Phase Annotation Using Adversarial Unsupervised Domain Adaptation Swathi Ananda, Yutaro Iwamoto, Xianhua HAN, Lanfen Lin, Hongjie Hu, Yen-Wei Chen, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Multi-phase computed tomography (CT) images are widely used for the diagnosis of liver disease, since different phase has different contrast enhancement (i.e., different domain), the multi-phase CT images should be annotated for all phase images for liver or tumor segmentation, which is a time-consuming and labor-expensive task. To lower the cost of manual annotation and domain shift problem, we propose an adversarial unsupervised domain adaptation (UDA) method for liver segmentation of multi-phase CT images with only single-phase annotation. The framework consists of two modules: a generator and a discriminator. We have employed U-Net as a generator as it is designed for medical image segmentation. We first use the annotated source images to train the generator only. Then we use the adversarial learning to train both generator and discriminator to minimize the difference between the source heatmap and target heatmap (domain shift). The refined generator is used for multi-phase CT image segmentation. To perform liver segmentation, initially, we have conducted experiments within each phase of our internal MPCT-FLL dataset (i.e., by utilizing the PV phase as the source and the ART phase as the target). Further, we use the publicly available LiTS dataset which consists of annotated PV phase images as the source domain, and each phase of our internal MPCT-FLL dataset i.e., (PV, ART, NC phase) as a target domain. The experimental results of this work suggest consistent and comparable improvement in the performance of our liver tumor segmentation over the previously reported state-of-the-art methods. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Multi-phase CT image / domain adaptation / adversarial learrning |
| Paper # | MI2022-38 |
| Date of Issue | 2022-07-01 (MI) |
| Conference Information | |
| Committee | MI |
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| Conference Date | 2022/7/8(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical imaging, recoginition, etc. |
| 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 | Takeshi Hara(Gifu Univ.) / Yoshito Otake(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Medical Imaging |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Short Paper] Multi-phase CT Image Segmentation with Single-Phase Annotation Using Adversarial Unsupervised Domain Adaptation |
| Sub Title (in English) | |
| Keyword(1) | Multi-phase CT image |
| Keyword(2) | domain adaptation |
| Keyword(3) | adversarial learrning |
| 1st Author's Name | Swathi Ananda |
| 1st Author's Affiliation | Ritsumeikan University(Swathi) |
| 2nd Author's Name | Yutaro Iwamoto |
| 2nd Author's Affiliation | Ritsumeikan University, Shiga, Japan(Yutaro) |
| 3rd Author's Name | Xianhua HAN |
| 3rd Author's Affiliation | Yamaguchi University, Japan(Xianhua) |
| 4th Author's Name | Lanfen Lin |
| 4th Author's Affiliation | Zhejiang University, Hangzhou, China(Lanfen) |
| 5th Author's Name | Hongjie Hu |
| 5th Author's Affiliation | Zhejiang University, Hangzhou, China(Hongjie) |
| 6th Author's Name | Yen-Wei Chen |
| 6th Author's Affiliation | Ritsumeikan University, Shiga, Japan(Yen-Wei) |
| Date | 2022-07-08 |
| Paper # | MI2022-38 |
| Volume (vol) | vol.122 |
| Number (no) | MI-98 |
| Page | pp.pp.24-25(MI), |
| #Pages | 2 |
| Date of Issue | 2022-07-01 (MI) |