Automatic worm detection to solve overlapping problems using a convolutional neural network.

The nematode Caenorhabditis elegans is a powerful experimental model to investigate vital functions of higher organisms. We recently established a novel method, named "pond assay for the sensory systems (PASS)", that dramatically improves both the evaluation accuracy of sensory response of worms and the efficiency of experiments. This method uses many worms in numbers that are impractical to count manually. Although several automated detection systems have been introduced, detection of overlapped worms remains difficult. To overcome this problem, we developed an automated worm detection system based on a deep neural network (DNN). Our DNN was based on a "YOLOv4″ one-stage detector with one-class classification (OCC) and multi-class classification (MCC). The OCC defined a single class for worms, while the MCC defined four classes for the number of overlapped worms. For the training data, a total of 2000 model sub-images were prepared by manually drawing square worm bounding boxes from 150 images. To make simulated images, a total of 10-80 model images for each class were randomly selected and randomly placed on a simulated microscope field. A total of 19,000 training datasets and 1000 validation datasets with a ground-truth bounding-box were prepared. We evaluated detection accuracy using 150 images, which were different from the training data. Evaluation metrics were detection error, precision, recall, and average precision (AP). Precision values were 0.91 for both OCC and MCC. However, the recall value for MCC (= 0.93) was higher than that for OCC (= 0.79). The number of detection errors for OCC increased with increasing the ground truth; however, that for MCC was independent of the ground truth. AP values were 0.78 and 0.90 for the OCC and the MCC, respectively. Our worm detection system with MCC provided better detection accuracy for large numbers of worms with overlapping positions than that with the OCC.