BioSR for LLS-SIM: Dataset of ER (metaphase of mitosis)

This is LLS-SIM dataset of ER (metaphase of mitosis), 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ER（有丝分裂中期）的LLS-SIM数据集，属于BioSR for LLS-SIM数据集的子集，数据集链接为https://doi.org/10.5281/zenodo.14322457。 BioSR for LLS-SIM是一套采用自研点阵光片结构照明显微镜（lattice light-sheet structured illumination microscopy，LLS-SIM）采集的生物图像数据集。目前该数据集包含多种生物结构的衍射极限LLSM图像与LLS-SIM成对图像，构成了高质量的体素级超分辨率（Super-Resolution, SR）数据集。 BioSR for LLS-SIM数据集源自我们于2024年发表的论文[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]，可作为元训练或其他基于深度学习的算法的配套数据集，为科研社区提供了验证本研究方法、复现实验结果以及开发适用于超分辨率点阵光片显微镜的自有算法的数据源。 BioSR for LLS-SIM数据集包含10个来自不同生物样本的独立任务数据集，分别对应：颗粒组分、染色体、纤维中心、纤连蛋白、溶酶体（Lyso）、微管（Microtubules, MTs）、花粉管肌动蛋白丝（F-actin）、线粒体内膜、贴壁Cos-7细胞内质网（ER）以及有丝分裂中期海拉（HeLa）细胞内质网（ER）。针对每类样本，我们采集了约30~50个感兴趣区域（region of interest, ROI）的原始LLS-SIM图像；对于每个ROI，我们采集了荧光强度从低到高的5个不同曝光等级的图像，并将最高荧光强度等级（即真值原始图像）通过传统LLS-SIM重建算法重构为高质量的真值LLS-SIM图像，可作为深度学习模型训练阶段的监督真值（ground truth, GT）。