Caenorhabditis elegans detection method based on multi-task deep neural network

Abstract

The invention discloses a caenorhabditis elegans detection method based on a multi-task deep neural network, and the method comprises the steps: obtaining an original fluorescence picture of caenorhabditis elegans, carrying out the manual marking of the fluorescence point contour and fluorescence brightness of the original fluorescence picture, carrying out the image expansion and preprocessing, and building a training set; an improved YOLACT network of a multi-task learning mechanism is used for learning, a binary mask and pixel coordinate information of fluorescent dots are obtained according to an instance segmentation result and a fluorescent brightness result of caenorhabditis elegans fluorescent dots of the improved YOLACT network and an instance segmentation result of caenorhabditis elegans polypide and fluorescent dots in an RGB image, and according to pixel coordinates of the binary mask, the fluorescent dots of the caenorhabditis elegans are obtained. And calculating the area of the fluorescent point. According to the detection method, three results are output through parallel learning of caenorhabditis elegans fluorescent dot segmentation and fluorescence degree and size measurement, the accuracy of the output results can be improved through mutual complementation among various loss functions in a multi-task learning mechanism, and the accuracy is improved while the learning efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for detecting C. elegans based on a multi-task deep neural network. Background technique [0002] C. elegans is a valuable model organism. Its nervous system is simple. The neurons (ie, fluorescent dots) of transgenic C. elegans can be visually observed through a microscope, and then the morphological changes of neurons can be detected to judge the damage of the nervous system. [0003] The traditional detection method is to manually calculate the number, area and fluorescence intensity of neurons, which is time-consuming and labor-intensive and has subjective interference. Applying computer methods to solve the problems of low efficiency and large errors in manual calculation and improving the accuracy of research results provide the possibility to promote the wider application of nematodes. [0004] YOLACT is a current deep convolutional neural network that e...

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Problems solved by technology

[0005] The existing methods usually manually judge the fluorescent points, calculate the number and size of the fluorescent points, the fluorescence intensity and other

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