Omkar2118/datasets huggingface dataset:
ACCT Data Repository
Full name: ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation

This dataset is a collection of fluorescent images from mice in order to test an automatic cell counting tool that we developed. 62 images viewed from 2 or 3 different fields of views are shown. In brief, the dataset was derived from brain sections of a model for HIV-induced brain injury (HIVgp120tg), which expresses soluble gp120 envelope protein in astrocytes under the control of a modified GFAP promoter. The mice were in a mixed C57BL/6.129/SJL genetic background, and two genotypes of 9 month old male mice were selected: wild type controls (Resting, n = 3) and transgenic littermates (HIVgp120tg, Activated, n = 3). No randomization was performed. HIVgp120tg mice show among other hallmarks of human HIV neuropathology an increase in microglia numbers which indicates activation of the cells compared to non-transgenic littermate controls. 

Brain sections were obtained using a vibratome (Leica VT1000S, Leica Biosystems, Buffalo Grove, IL) and cerebral cortex in 40 μm thick sagittal sections spaced 320 μm apart medial to lateral from brains of each genotype. Staining was performed with rabbit anti-ionized calcium-binding adaptor molecule 1 (Iba-1) IgG (1:125; Wako) with secondary antibody Fluorescein isothiocyanate (FITC). For quantification of Iba-1 stained microglia, cell bodies were counted in the cerebral cortex from three fields of view for three sections each per animal. Between 2 and 3 images were collected per field of view to capture as many cells as possible in sufficient focus for identification. Images were acquired at 10X magnification and pixel resolution 1280x1280 and cropped to 1280x733 pixel area to exclude irregular tissue edges. For more details, please refer to [ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation](https://doi.org/10.1038/s41598-023-34943-w).

We note that this data repository links to some images gathered in the [fluocells (Fluorescent Neuronal Cells)](https://paperswithcode.com/dataset/fluocells) dataset introduced by Morelli et al. which can be found here: [https://paperswithcode.com/dataset/fluocells](https://paperswithcode.com/dataset/fluocells). 

We provide a link to our automatic cell counting tool that this dataset was used for here at the following Github link: [https://github.com/tkataras/Automatic-Cell-Counting-with-TWS](https://github.com/tkataras/Automatic-Cell-Counting-with-TWS).

Introducing paper: ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation