| --- |
| dataset_info: |
| features: |
| - name: slice_id |
| dtype: int32 |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: label |
| dtype: int32 |
| splits: |
| - name: train |
| num_bytes: 288844280 |
| num_examples: 3064 |
| download_size: 292067828 |
| dataset_size: 288844280 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| - image-segmentation |
| language: |
| - en |
| tags: |
| - mri |
| - brain |
| - tumor |
| - cancer |
| - medical |
| - seg |
| - classification |
| pretty_name: Jun Cheng Brain Tumor Dataset |
| size_categories: |
| - 1K<n<10K |
| --- |
| # JCBTD (Jun Cheng Brain Tumor Dataset) — T1-Weighted Contrast-Enhanced Brain MRI with Tumor Segmentation |
|
|
| This dataset is derived from the **Jun Cheng Brain Tumor Dataset (JCBTD)**, a small-scale collection of T1-weighted contrast-enhanced brain MRI images with pathologically confirmed brain tumors, originally hosted on **Figshare**. The original collection provides brain MRI images in MATLAB format (.mat) with manually delineated tumor masks. |
| Here, we converted the original MATLAB files into PNG images and binary tumor masks. |
| The dataset is designed for brain tumor segmentation, and classification. |
|
|
|
|
| ## 📦 Dataset Structure |
|
|
| The dataset has a single `train` split, with each row corresponding to a single 2D axial slice from a patient's T1-weighted contrast-enhanced MRI: |
|
|
| | Field | Description | |
| |---|---| |
| | `slice_id` | Unique slice identifier (e.g., `1`, `2`, ..., `3064`) | |
| | `image` | 2D T1-weighted contrast-enhanced MRI slice (512 × 512 pixels, normalized to uint8) | |
| | `mask` | 2D binary tumor segmentation mask (512 × 512 pixels; 255 = tumor region, 0 = background) | |
| | `label` | Tumor type: `1` (meningioma), `2` (glioma), `3` (pituitary tumor) | |
|
|
| **Class Distribution:** |
| - Meningioma: 708 slices |
| - Glioma: 1,426 slices |
| - Pituitary tumor: 930 slices |
| - Total: 3,064 slices from 233 patients |
|
|
|
|
| ## ⚙️ Preprocessing |
|
|
| - Original MATLAB (.mat) files were loaded using `h5py`; each file contains a `cjdata` struct with image, tumor mask, and label fields |
| - Images were normalized to float32, rescaled to [0, 1], and converted to uint8 [0, 255] for storage as PNG |
| - Tumor masks were binarized (values > 0 → 255, all others → 0) to create binary segmentation masks |
| - All 3,064 slices were preserved |
| - Slice IDs correspond to the original MATLAB filename integers |
|
|
|
|
| ## 🚀 Usage |
|
|
| ```python |
| from datasets import load_dataset |
| import matplotlib.pyplot as plt |
| import numpy as np |
| |
| ds = load_dataset("chehablab/JCBTD", split="train") |
| |
| # Grab a sample slice and view image with mask overlay |
| sample = ds[0] |
| |
| fig, axes = plt.subplots(1, 3, figsize=(14, 4)) |
| |
| # Original image |
| axes[0].imshow(sample["image"], cmap="gray") |
| axes[0].set_title("T1-Weighted MRI") |
| axes[0].axis("off") |
| |
| # Tumor mask |
| axes[1].imshow(sample["mask"], cmap="gray") |
| axes[1].set_title("Tumor Mask") |
| axes[1].axis("off") |
| |
| # Image with mask overlay |
| axes[2].imshow(sample["image"], cmap="gray") |
| axes[2].imshow(sample["mask"], cmap="jet", alpha=0.4) |
| axes[2].set_title("MRI + Mask Overlay") |
| axes[2].axis("off") |
| |
| # Tumor classification metadata |
| tumor_labels = {1: "Meningioma", 2: "Glioma", 3: "Pituitary Tumor"} |
| info_text = ( |
| f"Slice ID: {sample['slice_id']}\n" |
| f"Tumor Type: {tumor_labels[sample['label']]}\n" |
| f"Patient ID: {sample['patient_id']}" |
| ) |
| fig.suptitle(info_text, fontsize=10) |
| plt.tight_layout() |
| plt.show() |
| |
| # Filter by tumor type |
| meningioma_slices = ds.filter(lambda x: x["label"] == 1) |
| print(f"Meningioma slices: {len(meningioma_slices)}") |
| ``` |
|
|
| --- |
|
|
| ## 🏷️ Feature Descriptions |
|
|
| ### Tumor Classification |
| Three distinct histopathological subtypes of primary brain tumors: |
|
|
| - **Meningioma** (`label=1`): 708 slices — Benign tumor arising from meninges (brain membranes); typically slow-growing with favorable prognosis |
| - **Glioma** (`label=2`): 1,426 slices — Malignant tumor arising from glial cells; includes high-grade aggressive variants with worse prognosis |
| - **Pituitary Tumor** (`label=3`): 930 slices — Tumor of the pituitary gland; often benign but may cause endocrine dysfunction |
|
|
| ### Image Specifications |
|
|
| - **Modality**: T1-weighted contrast-enhanced MRI (post-gadolinium DTPA injection) |
| - **In-plane resolution**: 512 × 512 pixels |
| - **Pixel dimensions**: 0.49 × 0.49 mm² |
| - **Slice thickness**: 6 mm |
| - **Slice gap**: 1 mm |
| - **Gadolinium dose**: 0.1 mmol/kg at 2 ml/s injection rate |
|
|
| ### Acquisition Protocol |
|
|
| All images were acquired at: |
| - **Nanfang Hospital**, Guangzhou, China |
| - **General Hospital**, Tianjin Medical University, China |
| - **Time period**: September 2005 – October 2010 |
|
|
| --- |
|
|
| ## 🧪 Use Cases |
|
|
| - Brain tumor segmentation from T1-weighted contrast-enhanced MRI |
| - Multi-class tumor classification (meningioma, glioma, pituitary) |
| - Tumor region detection and localization |
| - Representation learning and feature extraction for medical imaging |
| - Self-supervised pretraining on brain MRI |
| - Comparison of segmentation performance across tumor subtypes |
| - Data augmentation studies on tumor regions |
|
|
|
|
| ## ⚠️ Important Notes |
|
|
| - **Class imbalance**: Glioma is more prevalent (1,426 slices) compared to meningioma (708) and pituitary tumor (930). Use weighted sampling or class balancing as needed. |
| - **Tumor annotation**: Tumor masks are binary; voxels with value 255 indicate the tumor region as manually delineated by clinical experts. Background is 0. |
| - **Image normalization**: Images were normalized using min-max scaling (per-image); further normalization may be needed depending on your application. |
|
|
|
|
| ## 📚 Citation |
|
|
| This dataset is derived from data originally published on **Figshare** under a **CC BY 4.0** license. If you use this dataset, please aknowledge [chehablab.com](Chehab Lab) cite the original Jun Cheng Brain Tumor Dataset: |
|
|
| ```bibtex |
| @misc{Cheng2015JCBTD, |
| author = {Cheng, Jun}, |
| title = {Brain Tumor Dataset [dataset]}, |
| year = {2015}, |
| publisher = {Figshare}, |
| doi = {10.6084/m9.figshare.1512427} |
| } |
| ``` |
|
|
| Additionally, cite the research papers that introduced this dataset: |
|
|
| ```bibtex |
| @article{Cheng2015Enhanced, |
| author = {Cheng, Jun and others}, |
| title = {Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition}, |
| journal = {PLOS One}, |
| volume = {10}, |
| number = {10}, |
| year = {2015} |
| } |
| |
| @article{Cheng2016Retrieval, |
| author = {Cheng, Jun and others}, |
| title = {Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation}, |
| journal = {PLOS One}, |
| volume = {11}, |
| number = {6}, |
| year = {2016} |
| } |
| ``` |
|
|
| ## ✉️ Original Author Contact |
|
|
| **Jun Cheng** |
| School of Biomedical Engineering |
| Shenzhen University, Shenzhen, China |
| Email: chengjun583@qq.com |
|
|
| **GitHub**: https://github.com/chengjun583/brainTumorRetrieval (MATLAB source codes and additional resources) |
|
|
|
|
| ## 📜 License |
|
|
| This dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license, consistent with the original Jun Cheng Brain Tumor Dataset. |
|
|
| You may copy, modify, distribute, and use the data **for any purpose (commercial or non-commercial)**, **provided that appropriate credit is given** to Jun Cheng, Figshare, and the original papers. |
|
|
| [](https://creativecommons.org/licenses/by/4.0/) |
|
|
| **Chehab Lab @ 2026** |
