BioSR for LLS-SIM: Dataset of Fibrillar Center

This is LLS-SIM dataset of Fibrillar Center, which is a part of BioSR for LLS-SIM dataset (https://doi.org/10.5281/zenodo.14322457). BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy. The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models.本数据集为纤维中心（Fibrillar Center）相关的LLS-SIM数据集，是LLS-SIM版BioSR数据集的组成部分，数据集链接为https://doi.org/10.5281/zenodo.14322457。 LLS-SIM版BioSR是一套采用自研点阵光片结构化照明显微镜（lattice light-sheet structured illumination microscopy，LLS-SIM）采集的生物图像数据集。当前该数据集包含多种生物结构的衍射极限LLSM图像与LLS-SIM图像配对样本，构成了一套高质量三维超分辨率（super-resolution, SR）数据集。 LLS-SIM版BioSR数据集源自我们发表于2024年的论文[Qiao, C., Li, Z., Wang, Z., Lin, Y., 等 & Li, D. (2024). 用于快速、长期、近各向同性亚细胞成像的快速自适应超分辨率点阵光片显微镜. bioRxiv, 2024-05]，作为配套数据集用于元训练（meta-training）或其他基于深度学习的算法实现，为科研社区提供了复现我们的研究成果、测试我们提出的方法以及开发适用于超分辨率点阵光片显微镜的自主算法的数据源。 LLS-SIM版BioSR数据集包含来自10种不同生物样本的10个独立任务数据集，分别为：颗粒组分（granular component）、染色体（chromosomes）、纤维中心（Fibrillar Center）、纤维蛋白（fibrillarin）、溶酶体（Lyso）、微管（MTs）、花粉管中的肌动蛋白丝（F-actin in pollen tubes）、线粒体内膜（inner mitochondrial membrane）、贴壁Cos-7细胞内质网（ER in adherent Cos-7 cells）以及有丝分裂中期HeLa细胞内质网（ER in mitotic Hela cells during metaphase）。 针对每一类样本，我们采集了约30至50个感兴趣区域（region of interest, ROI）的原始LLS-SIM图像。对于每个感兴趣区域，我们采集了覆盖低到高荧光强度的5档不同光照条件下的图像，其中最高荧光强度档位的图像（即真值原始图像，GT raw images）通过常规LLS-SIM重建算法被重建为高质量的真值LLS-SIM图像，可作为深度学习模型训练阶段的标注真值（ground truth, GT）。