Automatic Image Analysis for Rapid Drug Susceptibility Testing

In recent years, a rapid increase in bacterial strains resistant to modern antibiotics has been observed. This alarming rise in drug-resistant organisms has emphasized the importance of identifying new effective antimicrobial agents. Since traditional approaches for drug susceptibility testing are time-consuming and labor-intensive, more ef cient methods are urgently needed. Here, we report an automatic image analysis system for drug susceptibility testing that provides results within 3 hours using a drug susceptibility testing micro uidic (DSTM) device. The device consists of ve sets of four micro uidic channels prepared by soft lithography. The channels are in close proximity to allow simultaneous observations. The antimicrobial agent and bacterial suspension to be tested are added to the channel and incubated for 3 hours. Previously, microscopic images of the DSTM device were analyzed manually by an expert to evaluate the susceptibility of a strain. In this work, we present an automatic computer vision algorithm for processing images and performing analysis. The algorithm enhances the quality of the input image, detects cells in each channel, extracts a variety of cell-related characteristics, and estimates drug susceptibility using a pre-trained support vector machine. We address the issue of overlapping cells by incorporating a graph-based cell separation algorithm. The minimum concentration of a drug for which the proposed method predicted susceptibility represents the minimum inhibitory concentration (MIC). The novel method was implemented as a standalone application and tested on a dataset containing images of 101 clinically isolated strains of Pseudomonas aeruginosa incubated in the presence of ve different drugs. The estimated MICs correlated well with the results obtained using the conventional broth microdilution method.