MAIS: Memory-Attention for Interactive Segmentation

• URL: http://arxiv.org/abs/2505.07511v1
• Date: Mon, 12 May 2025 12:48:27 GMT
• Title: MAIS: Memory-Attention for Interactive Segmentation
• Authors: Mauricio Orbes-Arteaga, Oeslle Lucena, Sabastien Ourselin, M. Jorge Cardoso,
• Abstract summary: Vision Transformer (ViT)-based models achieve state-of-the-art performance using user clicks and prior masks as prompts.<n>Existing methods treat interactions as independent events, leading to redundant corrections and limited refinement gains.<n>We address this by introducing Memory-Attention mechanism for Interactive that stores past user inputs and segmentation states, enabling temporal context integration.
• Score: 0.8678845273264675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interactive medical segmentation reduces annotation effort by refining predictions through user feedback. Vision Transformer (ViT)-based models, such as the Segment Anything Model (SAM), achieve state-of-the-art performance using user clicks and prior masks as prompts. However, existing methods treat interactions as independent events, leading to redundant corrections and limited refinement gains. We address this by introducing MAIS, a Memory-Attention mechanism for Interactive Segmentation that stores past user inputs and segmentation states, enabling temporal context integration. Our approach enhances ViT-based segmentation across diverse imaging modalities, achieving more efficient and accurate refinements.

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