基于草图图像编辑的交互式肿瘤进展建模
Interactive Tumor Progression Modeling via Sketch-Based Image Editing
AI总结：
本文提出SkEditTumor，一种基于草图的扩散模型，通过将草图作为结构先验实现可控的肿瘤进展编辑，在四个公开医学影像数据集上验证了其在图像保真度和分割精度上优于现有方法。
摘要：
在医学影像中准确可视化和编辑肿瘤进展对于诊断、治疗规划和临床沟通至关重要。为解决现有方法中存在的主观性和精度有限等挑战，我们提出SkEditTumor，一种基于草图的扩散模型，用于可控的肿瘤进展编辑。通过利用草图作为结构先验，我们的方法能够对肿瘤区域进行精确修改，同时保持结构完整性和视觉真实性。我们在四个公开数据集——BraTS、LiTS、KiTS和MSD-Pancreas——上评估了SkEditTumor，这些数据集涵盖了不同的器官和成像模态。实验结果表明，我们的方法优于最先进的基线方法，在图像保真度和分割精度方面均取得了优越表现。我们的贡献包括：将草图与扩散模型新颖地结合用于医学图像编辑，对肿瘤进展可视化实现细粒度控制，以及在多个数据集上的广泛验证，为该领域设立了新的基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
