This study introduces a structural-based training approach for CNN-based algorithms in single-molecule localization microscopy (SMLM) and 3D object reconstruction. We compare this approach with the traditional random-based training method, utilizing the LUENN package as our AI pipeline. The quantitative evaluation demonstrates significant improvements in detection rate and localization precision with the structural-based training approach, particularly in varying signal-to-noise ratios (SNRs). Moreover, the method effectively removes checkerboard artifacts, ensuring more accurate 3D reconstructions. Our findings highlight the potential of the structural-based training approach to advance super-resolution microscopy and deepen our understanding of complex biological systems at the nanoscale.

翻译：本研究引入了一种基于结构训练的方法,用于单分子定位显微镜(SMLM)和三维物体重建中基于CNN的算法。我们以LUENN包作为AI处理管线,将该方法与传统随机训练方法进行对比。定量评估表明,基于结构训练的方法在检测率和定位精度方面有显著提升,特别是在不同信噪比条件下。此外,该方法有效消除了棋盘格伪影,确保了更精确的三维重建。我们的研究结果凸显了基于结构训练方法在推进超分辨率显微镜技术及深化对复杂生物系统纳米尺度理解方面的潜力。