Efficient and precise cell counting for RNAi screening of Orientia tsutsugamushi infection using deep learning techniques

Abstract

Acquiring fluorescent scrub typhus images obtained through RNA interference screening for the analysis of 60 different human genes and 18 control genes poses challenges due to nonuniform or clumped cells and variations in image quality, rendering image processing (IP) counting inadequate. This study addresses three key questions concerning the application of deep learning methods to this dataset. Firstly, it explores the potential for object detection (OD) models to replace instance segmentation (IS) models in cell counting, striking a balance between accuracy and computational efficiency. Object detection models, including Faster R-CNN, You Only Look Once (YOLO), and Adaptive Training Sample Selection (ATSS) with reduced backbone sizes, outperform the instance segmentation model (Mask Region-Based Convolutional Neural Network: Mask R-CNN, Cascade Mask-RCNN) with both deep and shallow backbones. Notably, ATSS with Resnet-50 achieves an impressive mean average precision of 0.884 in just 33.1 milliseconds. Secondly, reducing the feature extractor size enhances cell counting efficiency, with OD models featuring reduced backbones demonstrating improved performance and faster processing. Finally, deep learning, especially OD models with shallow backbones, outperforms IP methods in both absolute and relative cell counting. This study demonstrates the potential for OD models to replace IS models, the efficiency gains achieved through the reduction of feature extractors, and the superiority of DL over IP in cell counting tasks.