BioTISR: Mitochondria (3D WF)

This dataset is part of BioTISR dataset.

BioTISR is a biological image dataset for super-resolution microscopy, currently including 2D and 3D time-lapse image pairs of low-and-high resolution images of a variety of biology structures, aiming to provide a high-quality dataset of time-lapse biological SR images for the community to spark more developments of computational SR methods.

At present, 2D dataset includes five specimens (clathrin-coated pits, lysosomes, outer mitochondrial membrane, microtubules, and F-actin) acquired with the GI/TIRF-SIM mode and nonlinear SIM mode of our Multi-SIM system, and 3D data includes three specimens (outer mitochondrial membrane, microtubules, and F-actin) acquired with 3D-SIM mode of the Multi-SIM system. For each type of specimen and each imaging modality, we acquired the raw data from at least 50 distinct regions-of-interest (ROI). For each ROI, we acquired two (3D data) or three (2D data) groups of N-phase × M-orientation × T-timepoint raw images with a constant exposure time but increasing the excitation light intensity, where (N, M, T) are (3, 3, 20) for TIRF-SIM and GI-SIM, (5, 5, 10) for nonlinear SIM, and (3, 5, 10) for 3D-SIM.

The BioTISR dataset is related to the following paper:Chang Qiao, Shuran Liu, Yuwang Wang, Wencong Xu, et al. "Time-lapse Image Super-resolution Neural Network with Reliable Confidence Evaluation for Optical Microscopy." bioRxiv 2024.05.04.592503 (2024), which is an extension of our previously published BioSR dataset (https://www.nature.com/articles/s41592-020-01048-5).

本数据集隶属于BioTISR数据集。

BioTISR是一款面向超分辨率显微镜的生物图像数据集，目前涵盖多种生物结构的高低分辨率2D与3D延时图像对，旨在为学界提供高质量的延时生物超分辨率图像数据集，以推动计算超分辨率方法的研发与进步。

目前，2D数据集包含5类生物标本：网格蛋白包被小窝、溶酶体、线粒体外膜、微管及丝状肌动蛋白（F-actin），采集自本团队Multi-SIM系统的GI/TIRF-SIM模式与非线性结构照明显微镜（Structured Illumination Microscopy, SIM）模式；3D数据集包含3类生物标本：线粒体外膜、微管及丝状肌动蛋白，采集自Multi-SIM系统的3D-SIM模式。

针对每一类标本与每一种成像模式，我们均从至少50个独立的感兴趣区域（Region of Interest, ROI）采集原始数据。针对每个感兴趣区域，我们采集了2组（3D数据集）或3组（2D数据集）N相位×M方位角×T时间点的原始图像序列，所有图像采用固定曝光时间、递增激发光强度的参数采集；其中TIRF-SIM与GI-SIM模式的(N,M,T)为(3,3,20)，非线性SIM模式为(5,5,10)，3D-SIM模式为(3,5,10)。

本BioTISR数据集关联以下论文：Chang Qiao、Shuran Liu、Yuwang Wang、Wencong Xu等撰写的《面向光学显微镜的带可靠置信度评估的延时图像超分辨率神经网络》，发表于bioRxiv，2024.05.04.592503（2024）。该数据集是本团队此前发表的BioSR数据集（https://www.nature.com/articles/s41592-020-01048-5）的扩展版本。