Panoramic Dental X-Ray Image Semantic Segmentation with TransUnet

The unofficial implementation of TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation on Pytorch

Output of my implementation. (A) Original X-Ray Image; (B) Merged Image of the Predicted Segmentation Map and Original X-Ray; (C) Ground Truth; (D) Predicted Segmentation Map

TransUNet

• On various medical image segmentation tasks, the ushaped architecture, also known as U-Net, has become the de-facto standard and achieved tremendous success. However, due to the intrinsic locality of convolution operations, U-Net generally demonstrates limitations in explicitly modeling long-range dependency. [1]
• TransUNet employs a hybrid CNN-Transformer architecture to leverage both detailed high-resolution spatial information from CNN features and the global context encoded by Transformers. [1]

Model Architecture

TransUNet Architecture Figure from Official Paper

Dependencies

• Python 3.6+
• pip install -r requirements.txt

Dataset

• UFBA_UESC_DENTAL_IMAGES[2] dataset was used for training.
• Dataset can be accessed by request[3].

Training

• Training process can be started with following command.
  • python main.py --mode train --model_path ./path/to/model --train_path ./path/to/trainset --test_path ./path/to/testset

Inference

• After model is trained, inference can be run with following command.
  • python main.py --mode inference --model_path ./path/to/model --image_path ./path/to/image

Other Implementations

• Self Attention CV / The AI Summer
• SOTA Vision / 04RR

References

• [1] TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• [2] Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives
• [3] GitHub Repository of Dataset