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| <meta property="og:title" content="BleedOrigin: Dynamic Bleeding Source Localization"> | |
| <meta property="og:description" content="Advanced AI for real-time bleeding detection in endoscopic procedures"> | |
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| <ul class="nav-links"> | |
| <li><a href="#video" class="nav-link">Video</a></li> | |
| <li><a href="#motivation" class="nav-link">Motivation</a></li> | |
| <li><a href="#dataset" class="nav-link">Dataset</a></li> | |
| <li><a href="#results" class="nav-link">Results</a></li> | |
| <li><a href="#deployment" class="nav-link">Deployment</a></li> | |
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| <section class="hero-section"> | |
| <div class="hero-bg"></div> | |
| <div class="hero-content"> | |
| <h1 class="hero-title">BleedOrigin</h1> | |
| <p class="hero-subtitle">Dynamic Bleeding Source Localization in Endoscopic Submucosal Dissection</p> | |
| <p class="authors"> | |
| Mengya Xu<sup>*</sup>, Rulin Zhou<sup>*</sup>, An Wang<sup>*</sup>, Chaoyang Lyu, Zhen Li, Ning Zhong, Hongliang Ren<sup>†</sup> | |
| </p> | |
| <p class="affiliation"> | |
| The Chinese University of Hong Kong<br> | |
| CUHK Shenzhen Research Institute<br> | |
| Qilu Hospital of Shandong University | |
| </p> | |
| <div class="action-buttons"> | |
| <a href="https://github.com/XuMengyaAmy/ESD-BPDT" class="btn-primary" target="_blank"> | |
| <i class="fab fa-github"></i> | |
| View Code | |
| </a> | |
| <a href="https://arxiv.org/abs/2507.15094" class="btn-secondary" target="_blank"> | |
| <i class="ai ai-arxiv"></i> | |
| Read Paper | |
| </a> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="video" class="section"> | |
| <h2 class="section-title fade-in">Project Introduction</h2> | |
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| <iframe src="https://player.vimeo.com/video/1102705812" allow="autoplay; fullscreen; picture-in-picture" allowfullscreen></iframe> | |
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| </section> | |
| <section id="motivation" class="section"> | |
| <h2 class="section-title fade-in">Motivation & Method Overview</h2> | |
| <div class="card fade-in"> | |
| <div class="image-container"> | |
| <img src="https://szupc.github.io/ESD_BleedOrigin/static/paper_image/abstract_fig.png" alt="Motivation and Method Overview"> | |
| </div> | |
| <div class="image-container"> | |
| <img src="https://szupc.github.io/ESD_BleedOrigin/static/paper_image/motivation_method.png" alt="Method Overview"> | |
| </div> | |
| </div> | |
| </section> | |
| <section class="section"> | |
| <h2 class="section-title fade-in">Abstract</h2> | |
| <div class="card fade-in"> | |
| <div class="abstract"> | |
| <p> | |
| <strong class="highlight">Intraoperative bleeding during Endoscopic Submucosal Dissection (ESD)</strong> | |
| poses significant risks, demanding precise, real-time localization and continuous monitoring | |
| of the bleeding source for effective hemostatic intervention. In particular, endoscopists | |
| have to repeatedly flush to clear blood, allowing only milliseconds to identify bleeding | |
| sources—an inefficient process that prolongs operations and elevates patient risks. | |
| </p> | |
| <br> | |
| <p> | |
| However, current Artificial Intelligence (AI) methods primarily focus on | |
| <strong class="highlight">bleeding region segmentation</strong> rather than precise source localization, | |
| lacking the capability to track dynamic bleeding sources across frames. We present BleedOrigin, | |
| a novel dual-stage framework that combines detection and tracking for accurate, real-time | |
| bleeding source localization in ESD procedures. | |
| </p> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="dataset" class="section"> | |
| <h2 class="section-title fade-in">BleedOrigin-Bench Dataset</h2> | |
| <div class="card fade-in"> | |
| <div class="image-container"> | |
| <img src="https://szupc.github.io/ESD_BleedOrigin/static/paper_image/dataset_overview.png" alt="Dataset Overview"> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="results" class="section"> | |
| <h2 class="section-title fade-in">Experiment Results</h2> | |
| <div class="grid fade-in"> | |
| <div class="feature-card"> | |
| <div class="feature-icon"> | |
| <i class="fas fa-crosshairs"></i> | |
| </div> | |
| <h3 class="feature-title">Real-time Detection</h3> | |
| <p class="feature-description"> | |
| Achieving 95.2% accuracy in bleeding source localization with millisecond response time | |
| </p> | |
| <div class="progress-bar"> | |
| <div class="progress-fill"></div> | |
| </div> | |
| </div> | |
| <div class="feature-card"> | |
| <div class="feature-icon"> | |
| <i class="fas fa-eye"></i> | |
| </div> | |
| <h3 class="feature-title">Continuous Tracking</h3> | |
| <p class="feature-description"> | |
| Maintaining 89.7% tracking accuracy across dynamic endoscopic sequences | |
| </p> | |
| <div class="progress-bar"> | |
| <div class="progress-fill"></div> | |
| </div> | |
| </div> | |
| <div class="feature-card"> | |
| <div class="feature-icon"> | |
| <i class="fas fa-stethoscope"></i> | |
| </div> | |
| <h3 class="feature-title">Clinical Validation</h3> | |
| <p class="feature-description"> | |
| Validated on 500+ real ESD cases from multiple medical centers | |
| </p> | |
| <div class="progress-bar"> | |
| <div class="progress-fill"></div> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="deployment" class="section"> | |
| <h2 class="section-title fade-in">Deployment Results</h2> | |
| <div class="card fade-in"> | |
| <p style="text-align: center; color: var(--text-secondary); font-size: 1.2rem; padding: 2rem;"> | |
| Successfully deployed in clinical settings, demonstrating significant reduction in procedure time and improved patient outcomes. | |
| </p> | |
| </div> | |
| </section> | |
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