Automatic Cell Segmentation by Adaptive Thresholding (ACSAT) for large scale calcium imaging datasets

摘要: Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds thousands individual neurons. With the continued neurotechnology development effort, is expected that millions could soon be simultaneously measured. An emerging technical challenge parallels advancement such a large processing correspondingly datasets, important step which identification Traditional methods rely mainly on manual or semi-manual inspection, cannot scaled datasets. To address this challenge, we developed automated cell segmentation method, referred as Automated Cell Segmentation by Adaptive Thresholding (ACSAT). ACSAT includes iterative procedure automatically calculates global and local threshold values during each iteration based pixel intensities. As such, algorithm capable handling morphological variations dynamic changes fluorescence intensities different In addition, computes adaptive time-collapsed representative sequence, thus provides results at fast speed. We tested wide-field datasets brain regions hippocampus striatum mice. achieved precision recall rates approximately 80% when compared are verified human inspection. Additionally, successfully detected low-intensity were initially undetected humans.