A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification

TL;DR

This paper presents an automated deep learning pipeline combining Mask R-CNN and YOLOv4 for efficient segmentation, detection, and classification of yeast cell compartments in fluorescence microscopy images, reducing manual effort.

Contribution

It introduces an end-to-end automated process integrating Mask R-CNN and YOLOv4 for microscopy image analysis, optimized for high-throughput biological data.

Findings

YOLOv4 achieves 98% F1-score in detection and classification.

Dividing images into quadrants improves detection accuracy.

Method is adaptable to medical multi-cell imaging.

Abstract

In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images in a short time is almost impossible. Detecting tiny individual cell compartments is one of many challenges faced by biologists. This paper aims at solving this problem by building an end-to-end process that employs methods from the deep learning field to automatically segment, detect and classify cell compartments of fluorescence microscopy images of yeast cells. With this intention we used Mask R-CNN to automatically segment and label a large amount of yeast cell data, and YOLOv4 to automatically detect and classify individual yeast cell compartments from these images. This fully automated end-to-end process is intended to be integrated into an interactive e-Science server in the PerICo1 project, which can be used by biologists with minimized human effort in training and operation to complete their various classification tasks. In addition, we evaluated the detection and classification performance of state-of-the-art YOLOv4 on data from the NOP1pr-GFP-SWAT yeast-cell data library. Experimental results show that by dividing original images into 4 quadrants YOLOv4 outputs good detection and classification results with an F1-score of 98% in terms of accuracy and speed, which is optimally suited for the native resolution of the microscope and current GPU memory sizes. Although the application domain is optical microscopy in yeast cells, the method is also applicable to multiple-cell images in medical applications