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Instance Segmentation and Teeth Classification in Panoramic X-rays

This repository contains the implementation and dataset related to the paper Instance Segmentation and Teeth Classification in Panoramic X-rays, submitted to Expert Systems with Applications Journal.

Dataset

  • We introduce a set of 425 panoramic X-rays with Human annotated Bounding Boxes and Polygons, the 425 images are a subset of UFBA-UESC Dental Dataset. This dataset can be extensively used for detection and segmentation tasks for Dental Panoramic X-rays. Refer to Description for understanding the organisation of annotations and panoramic X-rays. The Distribution of Categories in the dataset are metnioned in the table below.
Category 32 Teeth Restoration Dental Appliance Images Used Images
1 ✓ ✓ ✓ 73 24
2 ✓ ✓ 220 72
3 ✓ 45 15
4 ✓ 140 32
5 Images containing dental implant 120 37
6 Images containing more than 32 teeth 170 30
7 ✓ ✓ 115 33
8 ✓ 457 140
9 ✓ 45 7
10 115 35
Total 1500 425

Results

  • Teeth Numbering Results
Model Architecture mAP AP50
Mask R-CNN 70.5 97.2
Mask R-CNN + FCN 74.1 92.8
Mask R-CNN + pointRend 75.3 94.4
PANet 74.0 99.7
HTC 71.1 97.3
ResNeSt 72.1 96.8
YOLOv8 72.9 94.6
  • Instance Segmentation Results
Model Architecture Incisors Canines Premolars Molars
U-Net 73.29 69.92 67.62 64.98
Mask R-CNN 89.56 89.45 88.70 87.55
U-Net + Mask R-CNN 91.55 91.00 90.00 88.58
BB-UNet + YOLOv8 ( Test Dataset 1) 85.81 84.91 84.89 84.40
BB-UNet + YOLOv8 ( Test Dataset 2) 85.71 86.64 86.22 86.03
  • Refer to the paper for further information on model architectures and datasets used for evaluation.

Teeth Numbering Heatmaps

Teeth Numbering

Segmentation Masks

Segmentation Masks

Code Structure

2ddaatagen.ipynb                   => Notebook for generating labels
yolov8_train.ipynb                 => Notebook for training YOLOv8
yolo_test.ipynb                    => Notebook for testing YOLOv8
unet_training.ipynb                => Notebook for training U-Net
unet+cv.ipynb                      => Notebook for training U-Net with cross validation
yolov8+unet_training.ipynb         => Notebook for training BB-UNet
yolov8+unet+cv.ipynb               => Notebook for training BB-UNet with cross validation

Cite Us

Cite the paper if you find our work useful.

@misc{budagam2024instance,
      title={Instance Segmentation and Teeth Classification in Panoramic X-rays}, 
      author={Devichand Budagam and Ayush Kumar and Sayan Ghosh and Anuj Shrivastav and Azamat Zhanatuly Imanbayev and Iskander Rafailovich Akhmetov and Dmitrii Kaplun and Sergey Antonov and Artem Rychenkov and Gleb Cyganov and Aleksandr Sinitca},
      year={2024},
      eprint={2406.03747},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}