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Abstract <p>Medical imaging advancements have revolutionized clinical workflows; however, persistent challenges in annotation efficiency and cost continue to hinder scalable analysis. We propose a novel framework integrating the Segment Anything Model (SAM) with multimodal active learning to enhance breast cancer segmentation in Spectral Detector CT (SDCT) imaging. Our methodology combines SAM’s prompt-based segmentation capabilities with SDCT’s inherent dual-layer spectral consistency, enabling efficient annotation through expert-guided point prompts. Using a clinical dataset comprising 4996 slices from 162 breast cancer patients (32 test, 98 train, 32 validation), we demonstrate that multimodal integration of Iodine Density (IoD) and Z Effective (ZE) modalities achieves 96.75% of the performance level attained through expert manual annotations during model training, while reducing annotation costs by over 95%. Under intermediate annotation budgets, multi-click inputs and hybrid bounding box-click schemes consistently outperform single-click and box-only strategies, underscoring the value of richer spatial prompts for SAM. This SAM-driven active learning paradigm establishes a clinically viable pathway to maintain diagnostic-grade segmentation accuracy while significantly reducing resource-intensive manual annotation, thereby accelerating the translation of quantitative imaging biomarkers into routine breast cancer management.</p> Graphical abstract <p>Proposed Segment Anything Model-driven multimodal active learning framework for breast cancer segmentation in SDCT, enabling fast prompt-assisted auto-labeling and iterative refi nement.</p> <p></p>

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A segment anything model-driven framework for multimodal active learning in breast cancer segmentation

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Aisen Yang,
  • Jun Li,
  • Na Qin,
  • Deqing Huang,
  • Jian Shu,
  • Lulu Xu

摘要

Abstract

Medical imaging advancements have revolutionized clinical workflows; however, persistent challenges in annotation efficiency and cost continue to hinder scalable analysis. We propose a novel framework integrating the Segment Anything Model (SAM) with multimodal active learning to enhance breast cancer segmentation in Spectral Detector CT (SDCT) imaging. Our methodology combines SAM’s prompt-based segmentation capabilities with SDCT’s inherent dual-layer spectral consistency, enabling efficient annotation through expert-guided point prompts. Using a clinical dataset comprising 4996 slices from 162 breast cancer patients (32 test, 98 train, 32 validation), we demonstrate that multimodal integration of Iodine Density (IoD) and Z Effective (ZE) modalities achieves 96.75% of the performance level attained through expert manual annotations during model training, while reducing annotation costs by over 95%. Under intermediate annotation budgets, multi-click inputs and hybrid bounding box-click schemes consistently outperform single-click and box-only strategies, underscoring the value of richer spatial prompts for SAM. This SAM-driven active learning paradigm establishes a clinically viable pathway to maintain diagnostic-grade segmentation accuracy while significantly reducing resource-intensive manual annotation, thereby accelerating the translation of quantitative imaging biomarkers into routine breast cancer management.

Graphical abstract

Proposed Segment Anything Model-driven multimodal active learning framework for breast cancer segmentation in SDCT, enabling fast prompt-assisted auto-labeling and iterative refi nement.