BioSR for LLS-SIM: Dataset of ER (in adherent cells)

This is LLS-SIM dataset of ER (in adherent cells), 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.本数据集为贴壁细胞内的内质网（Endoplasmic Reticulum, ER）LLS-SIM数据集，隶属于LLS-SIM版BioSR数据集（https://doi.org/10.5281/zenodo.14322457）。 LLS-SIM版BioSR是一套基于自研晶格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晶格光片显微镜（Lattice Light-Sheet Microscopy, LLSM）图像与LLS-SIM图像配对数据，构成了一套高质量体视超分辨率数据集。 LLS-SIM版BioSR数据集源自本团队2024年5月发表于bioRxiv的论文《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晶格光片显微镜》（作者：Qiao C, Li Z, Wang Z, Lin Y 等, Li D），作为配套数据集可用于元训练或其他基于深度学习的算法研究，为学界提供了验证本团队方法、复现研究成果以及开发适用于超分辨率晶格光片显微镜的自有算法的数据源。 LLS-SIM版BioSR数据集包含来自10种不同生物样本的10组独立任务数据集，分别为：颗粒组分、染色体、纤维中心、纤维蛋白、溶酶体、微管、花粉管丝状肌动蛋白、线粒体内膜、贴壁Cos-7细胞内质网，以及有丝分裂中期的HeLa细胞内质网。 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。针对每个感兴趣区域，我们采集了荧光强度从低到高的5档不同光照条件下的图像；其中荧光强度最高的图像（即真值（Ground Truth, GT）原始图像）通过传统LLS-SIM重建算法处理为高质量的真值LLS-SIM图像，可作为深度学习模型训练阶段的标注真值。