AN END TO END PIPELINE TO LOCALIZE NUCLEI IN MICROSCOPIC ZEBRAFISH EMBRYO IMAGES
Determining the locations of nuclei in Zebrafish embryos is crucial for the study of the spatio-temporal behavior of these cells during the development process. With image seg- mentations, not only the location of the cell can be known, but also determine if each pixels is background or part of a nucleus. Traditional image processing techniques have been thor- oughly applied to this problem. These techniques suffer from bad generalization, many times relying on heuristic that apply to a specific type of image to reach a high accuracy when doing pixel by pixel segmentation. In previous work from our research lab, wavelet image segmentation was applied, but heuristics relied on expected nuclei size .
Machine learning techniques, and more specifically convolutional neural networks, have recently revolutionized image processing and computer vision in general. By relying on vast amounts of data and deep networks, problems in computer vision such as classification or semantic segmentation have reached new state of the art performance, and these techniques are continuously improving and pushing the boundaries of state of the art.
The lack of labeled data to as input to a machine learning model was the main bottleneck. To overcome this, this work utilized Amazon Turk platform. This platform allows users to create a task and give instructions to ‘Workers‘ , which agree to a price to complete each task. The data was preprocessed before being presented to the workers, and revised to make sure it was properly labeled.
Once labeled data was ready, the images and its corresponding segmented labels were used to train a U-Net model. In a nutshell, this models takes the input image, and at different scales, maps the image to a smaller vector. From this smaller vector, the model , again at different scales, constructs an image from this vector. During model training, the weights of the model are updated so that the image that is reconstructed minimizes the difference between the label image and the pixel segmentation.
We show that this method not only fits better the labeled ground truth image by the workers, but also generalizes well to other images of Zebrafish embryos. Once the model is trained, inference to obtain the segmented image is also orders of magnitude faster than previous techniques, including our previous wavelet segmentation method.