SLIP：用于3D医学图像的低延迟交互式提示分割

中文摘要 AI 辅助

交互式深度图像分割通过迭代细化用户提示（如正负点击）的预测来实现高效医学图像标注。近期基于补丁的方法存在交互延迟高、对连续交互响应有限及缺乏可逆提示支持等问题，评估也多依赖模拟而非真实用户交互研究。我们提出SLIP，一个用于交互式3D医学图像分割的端到端可训练框架，它将图像编码与提示引导的细化解耦。图像特征计算一次并复用，轻量级补丁内存库维护跨补丁共享的交互感知分割状态。这一表示能通过在整个图像中传播交互上下文来更新预测，支持可逆提示且无需重新计算图像特征，大幅降低交互延迟。我们训练单个SLIP模型用于跨多种解剖结构和成像模态的通用交互式分割。除标准模拟评估外，我们还进行了受控前瞻性用户研究，比较手动分割、nnInteractive和SLIP在三个临床标注任务、六位专家参与者及主观可用性指标方面的表现。SLIP在13个公共数据集上实现了SOTA交互式分割性能，同时提供比现有方法更低的交互延迟、更高的响应性、对可逆提示的支持及更高的用户偏好。

英文摘要

Interactive deep image segmentation enables efficient medical image annotation by iteratively refining predictions from user prompts, such as positive and negative clicks. Recent patch-based methods, including nnInteractive, achieve strong segmentation performance but remain limited in annotation workflows by high interaction latency, limited responsiveness to successive interactions, and the lack of support for reversible prompting. Furthermore, evaluation relies predominantly on simulated rather than controlled real-user interaction studies. We present SLIP, an end-to-end trainable framework for interactive 3D medical image segmentation that decouples image encoding from prompt-guided refinement. Image features are computed once and reused, while a lightweight patch memory bank maintains an interaction-aware segmentation state shared across patches. This representation enables prediction updates by propagating interaction context throughout the image, supports reversible prompting without recomputing image features, and substantially reduces interaction latency. By separating image representation from interactive reasoning, SLIP remains compatible with a wide range of image encoders. We train a single SLIP model for general interactive segmentation across diverse anatomical structures and imaging modalities. Beyond standard simulated evaluation, we conduct a controlled prospective user study comparing manual segmentation, nnInteractive, and SLIP across three clinical annotation tasks, six expert participants, and subjective usability measures, addressing the limited human validation of interactive segmentation methods. SLIP achieves SOTA interactive segmentation performance across 13 public datasets while providing lower interaction latency, greater responsiveness, support for reversible prompting, and higher user preference than existing approaches.

发表机构

• IRCAD France（法国国际消化道疾病研究中心）
• Université de Strasbourg, CNRS, ICube（斯特拉斯堡大学、法国国家科学研究中心、ICube实验室）
• IRCCS Humanitas Research Hospital（圣心研究医院）
• Humanitas University（圣心大学）
• Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS)（圣心综合大学医院）

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