Fig. 3: DL-based object detection and classification.
From: DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches
a A YOLOv2 model was trained to detect and classify different growth stages of live E. coli cells (i). “Dividing” cells (green bounding boxes) show visible septation, the class “Rod” (blue bounding boxes) represents growing cells without visible septation and regions with high cell densities are classified as “Microcolonies” (red bounding boxes). (ii) Three individual frames of a live cell measurement. b Antibiotic phenotyping using object detection. A YOLOv2 model was trained on drug-treated cells (i). The model was tested on synthetic images randomly stitched from patches of different drug treatments (ii). Bounding box colours in the prediction (iii) refer to the colour-code in (i). Vesicles (V, orange boxes) and oblique cells (O, green boxes) were added as additional classes during training. Mecillinam-treated cells were misclassified as MP265-treated cells (red arrows). Scale bars are 10 µm (a, overview), 3 µm (lower panel in a and b) and 1 µm (b, upper panel).
