DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis

The advent of deep-learning has set new standards in an array of image translation applications. At present, the use of these methods often requires computer programming experience. Non-commercial programs with graphical interface usually do not allow users to fully customize their deep-learning pipeline. Therefore, our primary objective is to provide a simple graphical interface that allows researchers with no programming experience to easily create, train, and evaluate custom deep-learning models for image translation. We also aimed to test the applicability of our tool in CT image semantic segmentation and noise reduction. DeepImageTranslator was implemented using the Tkinter library, the standard Python interface to the Tk graphical user interface toolkit; backend computations were implemented using data augmentation packages such as Pillow, Numpy, OpenCV, Augmentor, Tensorflow, and Keras libraries. Convolutional neural networks (CNNs) were trained using DeepImageTranslator. The effects of data augmentation, deep-supervision, and sample size on model accuracy were also systematically assessed. The DeepImageTranslator a simple tool that allows users to customize all aspects of their deep-learning pipeline, including the CNN, training optimizer, loss function, and the types of training image augmentation scheme. We showed that DeepImageTranslator can be used to achieve state-of-the-art accuracy and generalizability in semantic segmentation and noise reduction. Highly accurate 3D segmentation models for body composition can be obtained using training sample sizes as small as 17 images. In conclusion, an open-source deep-learning tool for accurate image translation with a user-friendly graphical interface was presented and evaluated. This standalone software can be downloaded at: https://sourceforge.net/projects/deepimagetranslator/.