Edge Detection Using Convolutional Neural Networks for Nematode Development and Adaptation Analysis

Abstract

The Antarctic nematode Plectus Murrayi is an excellent model organism for the study of stress and molecular mechanisms. Biologists analyze its development and adaptation by measuring the body length and volume. This work proposes an edge detection algorithm to automate this labor-intensive task. Traditional edge detection techniques use predefined filters to calculate the edge strength and apply a threshold to it to identify edge pixels. These classic edge detection techniques work independently of the image data and their results are sometimes inconsistent when edge contrast varies. Convolutional Neural Networks (CNNs) are regarded as powerful visual models that yield hierarchies of features learned from image data, and perform well for edge detection. Most CNNs based edge detection methods rely on classification networks to determine if an edge point exists at the center of a small image patch. This patch-by-patch classification approach is slow and inconsistent. In this paper, we propose an efficient CNN-based regression network that is able to produce accurate edge detection result. This network learns a direct end-to-end mapping between the original image and the desired edge image. This image-to-edge mapping is represented as a CNN that takes the original image as the input and outputs its edge map. The feature-based mapping rules of the network are learned directly from the training images and their accompanying ground truth. Experimental results show that this architecture achieves accurate edge detection and is faster than other CNN-based methods.