Researchers studying cardiovascular and metabolic disease in humans commonly use computer vision techniques to segment internal structures of the zebrafish animal model. However, there are no current image segmentation methods to target theeyes of the zebrafish. Segmenting the eyes is essential for accurate measurement ofthe eyes’ size and shape following the experimental intervention. Additionally, successful segmentation of the eyes functions as a good starting point for future segmentation of other internal organs. To establish an effective segmentation method, the deep learning neural network architecture, Deeplab, was trained using 275 images of the zebrafish embryo. Besides model architecture, the training was refined with proper data pre-processing, including data augmentation to add variety and toartificially increase the training data. Consequently, the results yielded a score of 95.88 percent when applying augmentations, and 95.30 percent without augmentations. Despite this minor improvement in accuracy score when using the augmented training dataset, it also produced visibly better predictions on a new dataset compared to the model trained without augmentations. Therefore, the implemented segmentation model trained with augmentations proved to be more robust, as the augmentations gave the model the ability to produce promising results when segmenting on new data.