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# Dataset
Here's a brief introduction of the dataset and disease that our MedSAM Adapter support now.
## 2D
### ISIC2016
This is a **2D** dataset of **melanoma** or **nevus** segmentation from dermoscopic images and contains **one** foreground category. Availabel here: [ISIC Challenge (isic-archive.com)](https://challenge.isic-archive.com/data/#2016)
### REFUGE2
This is a **2D** dataset of **optic disc** and **optic cup** segmentation over fundus images and contains **two** foreground category. Available here: [Program - Grand Challenge (grand-challenge.org)](https://refuge.grand-challenge.org/)
### LIDC
This is a **2D** dataset of **lung** images and contains **one** foreground category. Available here: [LIDC-IDRI Dataset | Papers With Code](https://paperswithcode.com/dataset/lidc-idri)
### DDTI
This is a 2D dataset for **thyroid nodule** segmentation and contains **one** foreground category . Available here: [DDTI: Thyroid Ultrasound Images (kaggle.com)](https://www.kaggle.com/datasets/dasmehdixtr/ddti-thyroid-ultrasound-images)****
### WBC
This is a **2D** dataset of **white blood cell **and contains **two** foreground category. Available here: [zxaoyou/segmentation_WBC: White blood cell (WBC) image datasets (github.com)](https://github.com/zxaoyou/segmentation_WBC)
This dataset contains 2 sub datasets, here we use `Dataset1`. You can change it in `dataset/wbc.py`.
### STARE
This is a **2D** dataset of **retinal blood vessel **and contains **one** foreground category. Available here: https://paperswithcode.com/dataset/stare
Can be appointed by `python train.py -dataset STARE ...`
### Pendal
This is a **2D** dataset of **mandible** and contains **one** foreground category. Available here: https://data.mendeley.com/datasets/hxt48yk462/2.
Can be appointed by `python train.py -dataset pendal ...`
This dataset contains 2 kind of segmentation labels, in folder `Segmentation1` and `Segmentation2`. Here we use the labels in `Segmentation1` as default. This can be changed in `dataset/pendal.py`.
## 3D
### Brat2021
This is a **3D** dataset of **brain tumors** that come from the MICCAI23 challenge and contains **three** foreground category. Available here: [MICCAI BRATS - The Multimodal Brain Tumor Segmentation Challenge](http://braintumorsegmentation.org/)
### Kits23
This is a **3D** dataset of **kidney tumors** that come from the MICCAI23 challenge and contains **two** foreground category. Available here: https://kits-challenge.org/kits21/. This dataset contains 2 kind of segmentation labels, namely aggregated_AND_seg.nii.gz, aggregated_OR_seg.nii.gz, aggregated_MAJ_seg.nii.gz. You can change it in `dataset/kits.py`.
Can be appointed by `python train.py -dataset kits ...`
### Atlas 23
This is a **3D** dataset of **liver tumors** that come from the MICCAI23 challenge and contains **two** foreground category. Available here: https://atlas-challenge.u-bourgogne.fr/dataset.
Can be appointed by `python train.py -dataset atlas ...`
### LNQ 23
This is a **3D** dataset of **mediastinal lymph node** that come from the MICCAI23 challenge and contains **one** foreground category. Available here: https://lnq2023.grand-challenge.org/ .
Can be appointed by `python train.py -dataset lnq ...`
### SegRap
This is a **3D** dataset of **nasopharynx cancer** from the MICCAI23 challenge and contains **53** foreground category. Available here: https://segrap2023.grand-challenge.org/segrap2023/
We use synthesized images`image.nii.gz` for each case in folder`SegRap2023_Training_Set_120cases`. As for the labels, we use the labels in `SegRap2023_Training_Set_120cases_OneHot_Labels\Task001`, you can try different kind of labels in the original dataset as well !
Can be appointed by `python train.py -dataset segrap ...`
### Toothfairy
This is a **3D** dataset of **inferior alveolar nerve** from the MICCAI23 challenge and contains **one** foreground category. Available here: https://toothfairy.grand-challenge.org/
Can be appointed by `python train.py -dataset toothfairy ...`