Seg and Ref: A Newly Developed Toolset for Artificial Intelligence-Powered Segmentation and Interactive Refinement for Labor-Saving Three-Dimensional Reconstruction.

Satoru Muro, Takuya Ibara, Akimoto Nimura, Keiichi Akita
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Abstract

Traditional three-dimensional reconstruction is labor-intensive owing to manual segmentation; this can be addressed by developing artificial intelligence-driven automated segmentation. However, it is limited by a lack of user-friendly tools for morphologists. We present a workflow for three-dimensional reconstruction using our artificial intelligence-powered segmentation tool. Specifically, we developed an interactive toolset, "Seg & Ref," to overcome the abovementioned challenges by enabling artificial intelligence-powered segmentation and easy mask editing without requiring a command-line setup. We demonstrated a three-dimensional reconstruction workflow using serial sections of a Carnegie Stage 15 human embryo. Automated segmentation (Step 1) was performed using the graphical user interface, "SAM2 GUI for Img Seq," which utilizes the Segment Anything Model 2 and supports interactive segmentation through a web-based interface. Users specify target structures via box prompts, and the results are propagated across all images for batch segmentation. The segmentation masks were reviewed and corrected (Step 2) using "Segment Editor PP," a PowerPoint-based tool enabling interactive mask refinement. Finally, the corrected masks were imported into the 3D Slicer application for reconstruction (Step 3). Our three-dimensional reconstruction visualized key structures, including the spinal cord, veins, aorta, mesonephros, gut, heart, trachea, liver, and peritoneal cavity. The reconstructed models accurately represented their spatial relationships and morphologies. This provides a labor-saving approach for three-dimensional reconstruction workflows owing to their optimization for serial sections, versatility, and accessibility without programming expertise. Therefore, morphological research can be enhanced by precise segmentation using intuitive and user-friendly interfaces of "Seg & Ref."

Seg and Ref:新开发的人工智能分割和交互式细化工具集,用于省力的三维重建。
传统的三维重建由于需要人工分割,劳动强度大;这可以通过开发人工智能驱动的自动细分来解决。然而,由于缺乏对形态学家用户友好的工具,它受到了限制。我们提出了一个使用人工智能分割工具进行三维重建的工作流程。具体来说,我们开发了一个交互式工具集“Seg & Ref”,通过启用人工智能驱动的分割和简单的掩码编辑来克服上述挑战,而无需命令行设置。我们演示了一个三维重建工作流程,使用卡内基阶段15人类胚胎的连续切片。自动分割(步骤1)使用图形用户界面“SAM2 GUI for Img Seq”执行,该界面利用了Segment Anything Model 2,并通过基于web的界面支持交互式分割。用户通过框提示指定目标结构,结果将传播到所有图像中进行批量分割。使用“段编辑器PP”对分割蒙版进行审查和纠正(步骤2),这是一种基于powerpoint的工具,可以进行交互式蒙版改进。最后,将校正后的口罩导入3D Slicer应用程序进行重建(step3)。我们的三维重建显示了关键结构,包括脊髓、静脉、主动脉、中肾、肠道、心脏、气管、肝脏和腹膜腔。重建的模型准确地反映了它们的空间关系和形态。这为三维重建工作流提供了一种省力的方法,因为它们对串行部分进行了优化,具有通用性,并且无需编程专业知识即可访问。因此,使用直观友好的“Seg & Ref”界面进行精确分割可以加强形态学研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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