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Interactive segmentation tools, such as SAM2, have shown strong performance in reducing annotation effort in natural images. However, unlike natural images, ultrasound images and videos often lack well-defined structure boundaries, which significantly degrade the performance of region-based point prompts in SAM models. To address these limitations, we introduce the Segment Anything Model 2 for UltraSound Annotation (SAMUSA). SAMUSA is based on SAM2 and introduces a new prompt strategy with boundary and temporal points, along with a novel boundary loss function, enabling the model to more efficiently segment structures with poorly defined boundaries, such as liver masses. We integrated SAMUSA as a 3D Slicer plugin, where it can be used for US videos and 3D US volumes segmentation. We present a prospective user study involving 6 participants (3 surgeons and 3 radiographers), which showed an average 34.1% annotation time reduction for image liver mass segmentation.

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SAMUSA: Segment Anything Model 2 for UltraSound Annotation

  • 会议论文

  • 发表日期：

    2026年

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  • Baptiste Podvin,
  • Toby Collins,
  • Güinther Saibro,
  • Chiara Innocenzi,
  • Yuchuan Yang,
  • Flavio Milana,
  • Yvonne Keeza,
  • Grace Ufitinema,
  • Florien Ujemurwego,
  • Guido Torzilli,
  • Jacques Marescaux,
  • Daniel George,
  • Alexandre Hostettler

摘要

Interactive segmentation tools, such as SAM2, have shown strong performance in reducing annotation effort in natural images. However, unlike natural images, ultrasound images and videos often lack well-defined structure boundaries, which significantly degrade the performance of region-based point prompts in SAM models. To address these limitations, we introduce the Segment Anything Model 2 for UltraSound Annotation (SAMUSA). SAMUSA is based on SAM2 and introduces a new prompt strategy with boundary and temporal points, along with a novel boundary loss function, enabling the model to more efficiently segment structures with poorly defined boundaries, such as liver masses. We integrated SAMUSA as a 3D Slicer plugin, where it can be used for US videos and 3D US volumes segmentation. We present a prospective user study involving 6 participants (3 surgeons and 3 radiographers), which showed an average 34.1% annotation time reduction for image liver mass segmentation.