BioSR for LLS-SIM: Dataset of Inner mitochondrial membrane

This is LLS-SIM dataset of Inner mitochondrial membrane, 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.

本数据集为线粒体内膜（inner mitochondrial membrane）相关的LLS-SIM数据集，属于BioSR for LLS-SIM数据集的一部分，相关DOI链接为https://doi.org/10.5281/zenodo.14322457。

BioSR for LLS-SIM是一套通过自研晶格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受限LLSM图像与LLS-SIM图像配对样本，构成了高质量的体素超分辨率（volumetric super-resolution，SR）数据集。BioSR for LLS-SIM数据集源自我们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晶格光片显微镜》, bioRxiv, 2024-05]，作为配套数据可用于开展元训练或其他基于深度学习的算法研究，可为学界提供测试我们的方法、复现我们的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。

BioSR for LLS-SIM数据集包含来自10种不同生物样本的10组多样化任务数据集，分别为颗粒组分、染色体、纤维中心、纤丝蛋白（fibrillarin）、溶酶体（Lyso）、微管（MTs）、花粉管F-肌动蛋白（F-actin in pollen tubes）、线粒体内膜、贴壁Cos-7细胞内质网（Endoplasmic Reticulum，ER）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30~50个感兴趣区域（Region of Interest，ROI）的原始LLS-SIM图像。针对每个ROI，我们采集了从低到高荧光强度的5个不同光强等级的图像，其中最高荧光强度等级的图像（即基准原始图像，GT raw images）通过传统LLS-SIM重建算法被重建为高质量的真实标注LLS-SIM图像，可作为深度学习模型训练阶段的真实标注（ground truth，GT）。