Interactive Tumor Progression Modeling via Sketch-Based Image Editing
This work addresses the need for precise and controllable tumor progression editing in medical imaging for diagnosis and treatment planning, representing an incremental advancement in the field.
The paper tackled the problem of visualizing and editing tumor progression in medical imaging by proposing SkEditTumor, a sketch-based diffusion model, which outperformed state-of-the-art baselines on four public datasets with superior image fidelity and segmentation accuracy.
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.