Updated on 2026/04/05

写真a

 
OU Kai
 
Name of department
Faculty of Systems Engineering, Informatics Division
Job title
Assistant Professor
External link

Education

  • -
    2026

    Wakayama University   Graduate School of Systems Engineering   システム工学専攻・博士後期課程  

  • -
    2023

    Wakayama University   Graduate School of Systems Engineering   システム工学専攻・博士前期課程  

Degree

  • 博士（工学）   2026

  • 修士（工学）   2023

Academic & Professional Experience

  • 2026.04
    -
    Now

    Wakayama University   Faculty of Systems Engineering   助教

Research Areas

  • Informatics / Perceptual information processing

  • Informatics / Intelligent informatics

Research Interests

  • パターン認識

  • マルチモーダル学習

  • 画像処理

  • セマンティックセグメンテーション

  • 深層学習

  • Few-shot学習

▼display all

Published Papers

  • Target Feature Augmentation and Background Optimization: Novel Approaches for Few-Shot Semantic Segmentation in Outdoor Scenes

    Kai Wang, Takayuki Nakamura （Part： Lead author )

    2025 IEEE International Conference on Real-time Computing and Robotics (RCAR) ( IEEE )    1054 - 1059   2025.06  [Refereed]

    DOI

  • SF-Net: Simultaneous Fusion Network for Semantic Segmentation and Depth Estimation

    Kai WANG, Takayuki NAKAMURA （Part： Lead author )

    IIEEJ Transactions on Image Electronics and Visual Computing   12 ( 2 ) 76 - 86   2025  [Refereed]

    DOI

  • A Multi‐Fusion Residual Attention U-Net Using Temporal Information for Segmentation of Left Ventricular Structures in 2D Echocardiographic Videos

    Kai Wang, Hirotaka Hachiya, Haiyuan Wu （Part： Lead author,　Corresponding author )

    International Journal of Imaging Systems and Technology ( Wiley )  34 ( 4 )   2024.07  [Refereed]

    　View Summary

    ABSTRACT

    The interpretation of cardiac function using echocardiography requires a high level of diagnostic proficiency and years of experience. This study proposes a multi‐fusion residual attention U‐Net, MURAU‐Net, to construct automatic segmentation for evaluating cardiac function from echocardiographic video. MURAU‐Net has two benefits: (1) Multi‐fusion network to strengthen the links between spatial features. (2) Inter‐frame links can be established to augment the temporal coherence of sequential image data, thereby enhancing its continuity. To evaluate the effectiveness of the proposed method, we performed nine‐fold cross‐validation using CAMUS dataset. Among state‐of‐the‐art methods, MURAU‐Net achieves highly competitive score, for example, Dice similarity of 0.952 (ED phase) and 0.931 (ES phase) in , 0.966 (ED phase) and 0.957 (ES phase) in , and 0.901 (ED phase) and 0.917 (ES phase) in , respectively. It also achieved the Dice similarity of 0.9313 in the EchoNet‐Dynamic dataset for the overall left ventricle segmentation. In addition, we show MURAU‐Net can accurately segment multiclass cardiac ultrasound videos and output the animation of segmentation results using the original two‐chamber cardiac ultrasound dataset MUCO.

    DOI

  • Study on Echocardiographic Image Segmentation Based on Attention U-Net

    Kai Wang, Jiwei Zhang, Hirotaka Hachiya, Haiyuan Wu （Part： Lead author )

    2022 IEEE International Conference on Mechatronics and Automation (ICMA) ( IEEE )    1091 - 1096   2022.08  [Refereed]

    DOI

Misc

  • A Few-shot Semantic Segmentation Method based on Feature Enhancement of Target Category

    WANG Kai, NAKAMURA Takayuki

    ロボティクスシンポジア予稿集   30th   2025

  • 深層学習を用いた心エコー動画における左心筋と左心腔の自動分画化

    上杉, 凌平, 吉野, 孝, 王, 開, 呉, 海元

    第85回全国大会講演論文集 ( 情報処理学会 )  2023 ( 1 ) 511 - 512   2023.02

    　View Summary

    心疾患の診断方法として心エコー動画を用いることがよくあるが，この診断は専門医にしか行うことができずまた専門医への負担が大きい．そこで心エコー動画を見やすくするために自動でアノテーションを行う手法を提案する．まず手動でアノテーションを行った心エコー画像をもとに深層学習を用いて学習モデルを作成し，その後モデルの評価を行い，心エコー画像と心エコー動画に自動的にアノテーションを行うことでアノテーションされた画像や動画を得ることができる．

  • 領域分割と深度推定の同時学習によるRGB画像のセマンティックセグメンテーションの精度向上

    王開, 中村恭之

    日本ロボット学会学術講演会予稿集(CD-ROM)   41st   2023

  • Automated Segmentation of Left Ventirular Wall and Extraction of Left Ventricular Cavity in Echocardiographic Images by Deep Learning

    WANG Kai, ZHANG Jiwei, 穂積健之, WU Haiyuan

    電子情報通信学会技術研究報告(Web)   120 ( 409(PRMU2020 69-101) )   2021

  • Media detection from cardiovascular OCT images based on deep learning

    ZHANG Jiwei, WANG Kai, 久保隆史, WU Haiyuan

    電子情報通信学会技術研究報告(Web)   120 ( 409(PRMU2020 69-101) )   2021