用草图控制肿瘤进展生成，让医学影像编辑更精准真实
Interactive Tumor Progression Modeling via Sketch-Based Image Editing
- 用草图作为结构先验，指导扩散模型精准编辑肿瘤区域
- 在4个公开数据集上实现更高图像保真度与分割精度
- 适合医学影像编辑、临床辅助诊断等需要精细调控的场景
准确可视化和编辑医学影像中的肿瘤进展对诊断、治疗规划和临床沟通至关重要。为解决现有方法主观性强、精度不足的问题，我们提出 SkEditTumor——一种基于草图的扩散模型，实现可控的肿瘤进展编辑。通过将草图作为结构先验，该方法可在保持解剖结构完整性和视觉真实性的同时，精确修改肿瘤区域。我们在 BraTS、LiTS、KiTS 和 MSD-Pancreas 四个公开数据集上进行了评估，覆盖多种器官和成像模态。实验结果表明，该方法优于现有先进基线，在图像保真度和分割准确性方面均表现更优。贡献包括：首次将草图与扩散模型结合用于医学图像编辑，实现对肿瘤进展可视化的细粒度控制，并在多个数据集上完成全面验证，为该领域树立了新基准。
原文摘要 · Abstract (English)
Accurately visualizing and editing tumor progression in medical imaging is crucial for diagnosis, treatment planning, and clinical communication. To address the challenges of subjectivity and limited precision in existing methods, we propose SkEditTumor, a sketch-based diffusion model for controllable tumor progression editing. By leveraging sketches as structural priors, our method enables precise modifications of tumor regions while maintaining structural integrity and visual realism. We evaluate SkEditTumor on four public datasets - BraTS, LiTS, KiTS, and MSD-Pancreas - covering diverse organs and imaging modalities. Experimental results demonstrate that our method outperforms state-of-the-art baselines, achieving superior image fidelity and segmentation accuracy. Our contributions include a novel integration of sketches with diffusion models for medical image editing, fine-grained control over tumor progression visualization, and extensive validation across multiple datasets, setting a new benchmark in the field.
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