InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation
InstEditSeg uses instruction-driven image editing for polyp and skin lesion segmentation, significantly enhancing cross-domain generalization.
Key Findings
Methodology
InstEditSeg redefines medical image segmentation as an instruction-driven image editing task. It uses DINOv3 as an auxiliary visual encoder to build a multi-scale feature pyramid, which is fused with the diffusion U-Net. A dual-branch classifier-free guidance strategy reduces inference cost.
Key Results
- On the PolypGen dataset, InstEditSeg achieved a Dice coefficient of 83.92%, significantly outperforming other methods, demonstrating strong cross-domain generalization.
- On the CVC-ColonDB dataset, InstEditSeg achieved a Dice coefficient of 92.95%, surpassing all compared methods.
- On the ISIC2017 dataset, InstEditSeg led with a Dice coefficient of 83.14%, proving effective in skin lesion segmentation.
Significance
InstEditSeg addresses limitations of traditional segmentation methods in low contrast and blurry boundaries, particularly excelling in cross-domain generalization. It offers a new perspective for medical image segmentation by combining generative model visual priors and instruction-driven task control.
Technical Contribution
InstEditSeg is the first to apply instruction-driven image editing to medical segmentation, eliminating task-specific heads and leveraging latent diffusion model priors for more complete multi-lesion segmentation and task control.
Novelty
InstEditSeg is the first framework to redefine medical image segmentation as an instruction-driven image editing task, overcoming limitations of traditional segmentation methods by utilizing generative model latent visual priors.
Limitations
- InstEditSeg is color-sensitive, which may lead to inaccurate segmentation results in certain scenarios.
- Attribute-conditioned selection is unsupported, limiting task flexibility.
Future Work
Future research could explore InstEditSeg's application in other medical image domains and optimize its instruction comprehension across different tasks.
AI Executive Summary
InstEditSeg uses instruction-driven image editing for polyp and skin lesion segmentation, addressing limitations of existing methods in low contrast and blurry boundaries. The framework leverages DINOv3 as an auxiliary visual encoder to build a multi-scale feature pyramid, which is fused with the diffusion U-Net, significantly enhancing cross-domain generalization.
Across multiple datasets, InstEditSeg demonstrates superior segmentation performance, especially on unseen data. Its generative framework not only improves segmentation accuracy but also achieves more complete multi-lesion segmentation and task control.
Despite limitations in color sensitivity and attribute-conditioned selection, its innovative instruction-driven approach offers a new perspective for medical image segmentation, with future research potentially optimizing its application across different tasks.
Deep Analysis
Background
Medical image segmentation is crucial for clinical diagnosis, with traditional methods like U-Net becoming standard but performing poorly in low contrast and blurry boundaries. Generative models offer new possibilities, especially with diffusion models in image generation.
Core Problem
Existing methods struggle with low contrast and blurry boundaries, particularly lacking cross-domain generalization. Accurate segmentation of polyps and skin lesions is vital for early diagnosis.
Innovation
InstEditSeg redefines medical image segmentation as an instruction-driven image editing task, leveraging generative model visual priors, eliminating task-specific heads, and achieving more complete multi-lesion segmentation.
Methodology
- �� Use DINOv3 as an auxiliary visual encoder to build a multi-scale feature pyramid.
- �� Fuse with diffusion U-Net through channel concatenation and zero-initialized convolution to inject hierarchical discriminative priors.
- �� Employ a dual-branch classifier-free guidance strategy to reduce inference cost.
Experiments
Experiments conducted on multiple datasets including Kvasir-SEG, CVC-ClinicDB, PolypGen, and ISIC2017. Dice coefficient and IoU used as primary evaluation metrics, comparing performance across different methods.
Results
InstEditSeg achieved a Dice coefficient of 83.92% on the PolypGen dataset, significantly outperforming other methods. On the CVC-ColonDB dataset, it achieved a Dice coefficient of 92.95%, surpassing all compared methods.
Applications
InstEditSeg can be used for clinical diagnosis in polyp and skin lesion segmentation, particularly excelling in cross-domain generalization tasks.
Limitations & Outlook
InstEditSeg is color-sensitive, which may lead to inaccurate segmentation results in certain scenarios. Attribute-conditioned selection is unsupported, limiting task flexibility.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking, and InstEditSeg is like a smart chef assistant. It can cut ingredients into the shapes and colors you want based on your instructions, rather than simply splitting them in half. This way, even if the ingredients are complex, it can accurately complete the task.
ELI14 Explained like you're 14
Hey, pals! Did you know InstEditSeg is like a super-smart game character that can find hidden treasures on the game map based on your instructions? Not only can it find the treasures, but it can also tell you their color and shape. Isn't that cool?
Glossary
InstEditSeg
A framework that redefines medical image segmentation as instruction-driven image editing.
Used for polyp and skin lesion segmentation.
DINOv3
An encoder used to extract visual features, aiding in recovering fine anatomical structures.
Used as an auxiliary visual encoder.
Diffusion Model
A generative model that achieves image generation through iterative denoising.
Used for image editing and segmentation.
Dice Coefficient
A metric for evaluating segmentation accuracy, with higher values indicating better performance.
Used for evaluating segmentation performance.
Cross-Domain Generalization
The model's performance capability on unseen data.
InstEditSeg's performance on the PolypGen dataset.
Open Questions Unanswered questions from this research
- 1 How can InstEditSeg's application in different medical image domains be further optimized?
- 2 How can InstEditSeg's color sensitivity limitations be addressed?
- 3 How can attribute-conditioned selection be supported to enhance task flexibility?
Applications
Immediate Applications
Clinical Diagnosis
InstEditSeg can be used for automatic segmentation of polyps and skin lesions, improving diagnostic efficiency.
Long-term Vision
Medical Image Analysis
InstEditSeg has the potential to be applied to broader medical image analysis tasks, advancing automated healthcare.
Abstract
Accurate segmentation of polyps and skin lesions is pivotal for clinical diagnosis, yet existing methods struggle with low contrast, ambiguous boundaries, and cross-domain distribution discrepancies. Discriminative networks and most diffusion-based segmentation approaches predict standalone binary masks, leaving the visual priors of large-scale pretrained generative models largely unexploited. We propose InstEditSeg, a unified generative framework that reformulates medical segmentation as an instruction-driven image editing problem. Instead of emitting a mask, the model renders a color-coded overlay on the original image, conditioned on a textual instruction, so that the edited output aligns with the natural image distribution learned by latent diffusion models and mitigates the domain gap between natural and medical imagery. To recover fine anatomical structures, we introduce DINOv3 as an auxiliary visual encoder and a DINO Feature Guidance Block that builds a multi-scale feature pyramid. The pyramid is fused into the diffusion U-Net by channel concatenation and zero-initialized convolution so that hierarchical discriminative priors can be injected without perturbing the pretrained weights. A dual-branch classifier-free guidance strategy requiring only two forward passes per denoising step reduces inference cost. On polyp and skin lesion benchmarks the framework achieves accuracy competitive with strong discriminative baselines, and it further demonstrates concrete advantages of the generative formulation: notably better cross-domain generalization on unseen data, more complete multi-lesion segmentation, instruction-conditioned task control, and sampling flexibility. We also analyze the strengths and limitations of the paradigm, including its color sensitivity and unsupported attribute-conditioned selection. Code is available at: https://github.com/wincharm001/InstEditSeg.