Teeth Segmentation Using UNet and Its Variants

This repository contains the code for training and evaluating various UNet models for teeth segmentation. The models implemented include the original UNet, as well as some of its variants such as UNet++, ResUNet, and Attention UNet.

Data

The data used for this project is not publicly available, but you can request it by contacting me through the email address provided on the profile page. Once you have the data, make sure to update the paths accordingly.

Usage

Before running the code, make sure to modify the 'train.py' file and the other relevent files so that you can get the relevent results.

To train the model, simply run the train.py script:

python train.py 

Results for the test data

UNet Variants	Test Accurary	Test Dice Score
Base UNet	96.10	90.47
UNet with GN	96.71	91.53
Attention UNet	96.40	91.01
Spatial Attention UNet	96.45	91.09
Inception UNet	96.29	90.69
Residual UNet	96.16	90.06
UNet++	96.11	90.33
Dense UNet with GN	96.77	91.88
Spatial Attention UNet2 ${\color{red}^*}$	97.32	93.12

${\color{red}*}$ increase the resolution from 256*256 to 768*512, reduce the batch size from 16 to 2, used Group Normalization and Custom spatial attention module with base UNet

${\color{red}Note}$ This project is solely for learning purposes; no standard practices are applied. Therefore, I am not claiming any state-of-the-art results.