kjw9899 / dentexseganddet

DentexSegAndDet

This repository contains our algorithm for the MICCAI 2023 Dentex challange.

• Method paper: Intergrated Segmentation and Detection Models for Dentex Challenge 2023 (arxiv.org)

• Dataset structure Datasets are organized as:

  dentex_dataset
  ├── coco
  │   ├── disease
  │   │   ├── annotations
  │   │   ├── train2017
  │   │   └── val2017
  │   ├── disease_all
  │   │   ├── annotations
  │   │   ├── train2017
  │   │   └── val2017
  │   ├── enumeration32
  │   │   ├── annotations
  │   │   ├── train2017
  │   │   └── val2017
  │   └── quadrant
  │       ├── annotations
  │       ├── train2017
  │       └── val2017
  ├── origin
  │   ├── quadrant
  │   ├── quadrant_enumeration
  │   ├── quadrant_enumeration_disease
  │   └── unlabelled
  ├── segmentation
  │   ├── enumeration32
  │   │   ├── masks
  │   │   └── xrays
  │   └── enumeration9
  │       ├── masks
  │       └── xrays
  └── yolo
      ├── disease
      │   ├── images
      │   │   ├── train2017
      │   │   └── val2017
      │   └── labels
      │       ├── train2017
      │       └── val2017
      └── disease_all
          ├── images
          │   ├── train2017
          │   └── val2017
          └── labels
              ├── train2017
              └── val2017
  

• Process:

  • prepare detection dataset

    Run each process... function in process_dataset.py to convert the dataset to expected format (coco or yolo). The processes are intended to be executed sequentially.

  • train detection models

    Download pretrained weights from each offical repos(swin-transformer, dino, yolo, etc.) and refer to those offical repos and command_snippets.sh to train detection models.

  • prepare segmentaion dataset

    32-class segmentaion dataset can be generated from the origin dataset. 9-class segmentation dataset depends on the prediction result by a quadrant detection model. See results/enumeration_dataset_quadrant_predictions.json for example.

  • train segmentaion models

    Refer to the command_snippets.sh

  • run prediction

    Choose best checkpoints for each model, rename them or modify the paths in the predict.py, and run predict.py. results/abnormal-teeth-detection.json is an example output.