Project under: Origin health, singapore: http://www.originhealth.ai/
Welcome to the Github repository for Manual-Image-segmentation-GUI! This project was developed during the period of Sep 2020 to Dec 2020 under the supervision of Origin Health, Singapore, with a goal to achieve automatic segmentation of fetal ultrasound images through the use of deep learning segmentation methods. In order to train the segmentation model, we needed to generate a dataset consisting of training pairs: input ultrasound images and segmented labels. While input ultrasound images were provided by medical centers, segmented labels needed to be generated manually. To reduce costs, we proposed the idea of creating a GUI for manual image segmentation.
We developed two versions of the tool using Pytkinter and PyQt, which allow users to load images for segmentation and achieve multiclass manual segmentation using different colors for each class. The tool also features two types of brushes (solid brush tool and Lazo brush tool), two different shapes of brushes (circular and square), and allows for the adjustment of the histogram of the image. Additionally, the image can be zoomed and measurements can be taken between two points for data collection purposes. Users can register comments regarding the image in the dataset to aid in the removal of poor images.
Other features of the tool include the smoothening of segmented regions, the removal of faulty islands of segments with customized sizes, undo and redo options, and the ability to save a checkpoint of the previously segmented image so that segmentation of a series of image data can be done with breaks. The tool also has code to encrypt the data, allowing images to be sent to the user in an encrypted way as a numpy file so that the user can open the tool and load the image without needing to store the images separately.
This tool is not restricted to ultrasound images alone and can be used to generate segmented labels for any semantic segmentation project. The tool is available in exe format in the folder, and we hope that it proves to be a valuable asset to the community of researchers and developers.
