MIQ-SAM3D: From Single-Point Prompt to Multi-Instance Segmentation via Competitive Query Refinement

• URL: http://arxiv.org/abs/2511.01345v1
• Date: Mon, 03 Nov 2025 08:48:28 GMT
• Title: MIQ-SAM3D: From Single-Point Prompt to Multi-Instance Segmentation via Competitive Query Refinement
• Abstract summary: We propose MIQ-SAM3D, a multi-instance 3D segmentation framework with a competitive query optimization strategy.<n>A prompt-conditioned instance-query generator transforms a single point prompt into multiple specialized queries.<n>A hybrid CNN-Transformer encoder injects CNN-derived boundary saliency into ViT self-attention via spatial gating.<n>A competitively optimized query decoder then enables end-to-end, parallel, multi-instance prediction.
• Score: 0.328418927821443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate segmentation of medical images is fundamental to tumor diagnosis and treatment planning. SAM-based interactive segmentation has gained attention for its strong generalization, but most methods follow a single-point-to-single-object paradigm, which limits multi-lesion segmentation. Moreover, ViT backbones capture global context but often miss high-fidelity local details. We propose MIQ-SAM3D, a multi-instance 3D segmentation framework with a competitive query optimization strategy that shifts from single-point-to-single-mask to single-point-to-multi-instance. A prompt-conditioned instance-query generator transforms a single point prompt into multiple specialized queries, enabling retrieval of all semantically similar lesions across the 3D volume from a single exemplar. A hybrid CNN-Transformer encoder injects CNN-derived boundary saliency into ViT self-attention via spatial gating. A competitively optimized query decoder then enables end-to-end, parallel, multi-instance prediction through inter-query competition. On LiTS17 and KiTS21 dataset, MIQ-SAM3D achieved comparable levels and exhibits strong robustness to prompts, providing a practical solution for efficient annotation of clinically relevant multi-lesion cases.

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