Wakayama University · Faculty of Systems Engineering · 助教

Research keywordsマルチモーダル学習・画像処理・intelligence・Segmentation・パターン認識・セマンティックセグメンテーション・深層学習・Artificial

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• A few-shot semantic segmentation method based on feature enhancement of target category2026 · Kai Wang, Takayuki Nakamura (担当区分： 筆頭著者, 責任著者 )
• Target Feature Augmentation and Background Optimization: Novel Approaches for Few-Shot Semantic Segmentation in Outdoor Scenes2025 · Kai Wang, Takayuki Nakamura (担当区分： 筆頭著者 )
• SF-Net: Simultaneous Fusion Network for Semantic Segmentation and Depth Estimation2025 · Kai WANG, Takayuki NAKAMURA (担当区分： 筆頭著者 )
• A Multi‐Fusion Residual Attention U-Net Using Temporal Information for Segmentation of Left Ventricular Structures in 2D Echocardiographic Videos2024 · 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.

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