Improved Segmentation of Confocal Calcium Videos of Hela Cells Using Deep-Learning-Assisted Watershed Algorithm.

The potential of calcium imaging in high-throughput drug screening experiments remains underutilized, primarily because of time-intensive manual identification of cells.To overcome these challenges, we propose to use deep learning enhanced watershed segmentation, wherein the complex morphology and the distinctions in the time courses are appropriately accounted through enhanced watershed and multi-frame processing respectively. Our segmentation pipeline involved training of two CNNs (modified U-Net and YOLOv5), one to predict distance transform and other to detect individual cells on the predicted distance transform images.

Towards Fluorescent-Tag-Less Viral Titration: Automated Estimation of Cell-Size Distribution and Infection Level from Phase-Contrast Microscopy Using Deep Learning and Transfer Learning.

Automated detection of infected insect cells is one of the crucial tasks in the field of recombinant protein production and vaccine development. The major challenge lies in manual segmentation of cells and quantifying cell size distribution is tedious and requires extensive effort. While such assessment of the infection levels is possible through fluorescent imaging, it requires tagging the expressed protein with a fluorescent marker.