Dental Image segmentation
dataset consists of a total of 989 images, which were randomly divided into training, validation, and test sets with a split of 70% for training, 20% for validation, and 10% for testing. This division was achieved using a Python script named data_split.py, which also saves dataset lists in the data.json file. Additionally, it stores class names and class weights for segmentation.
evaluation.py
computes evaluation metrics over the training, validation, and test datasets and saves the Dice score for each image in the file evaluation_results.csv
gui.py
renders the gui for to uplload image and visualize segmentation and save file.