GBM-Reservoir数据集及分割标注

数据集获取（Figshare）

• 源站标题：GBM-Reservoir: Dataset and Segmentations
• DOI：10.6084/m9.figshare.28001450.v20
• 发布日期：2024-12-18
• 文件数：80 个
• 文件总体积：379.86 GB
• 源站页面：https://figshare.com/articles/dataset/GBM-Reservoir_Dataset_and_Segmentations/28001450

命令行下载

curl -L -o "GBMR-example-0000-0002.png" "https://ndownloader.figshare.com/files/51086270"
curl -L -o "GBMR-example-0000-0002-video.mp4" "https://ndownloader.figshare.com/files/51086276"
curl -L -o "GBM_Reservoir_1.zip" "https://ndownloader.figshare.com/files/51101582"
curl -L -o "GBM_Reservoir_2.zip" "https://ndownloader.figshare.com/files/51101591"
curl -L -o "GBM_Reservoir_3.zip" "https://ndownloader.figshare.com/files/51101585"
curl -L -o "GBM_Reservoir_4.zip" "https://ndownloader.figshare.com/files/51101588"
curl -L -o "GBM_Reservoir_5.zip" "https://ndownloader.figshare.com/files/51131045"
curl -L -o "GBM_Reservoir_6.zip" "https://ndownloader.figshare.com/files/51131048"

文件清单

• GBMR-example-0000-0002.png（433.99 KB）
• GBMR-example-0000-0002-video.mp4（54.59 MB）
• GBM_Reservoir_1.zip（4.89 GB）
• GBM_Reservoir_2.zip（4.89 GB）
• GBM_Reservoir_3.zip（4.89 GB）
• GBM_Reservoir_4.zip（4.89 GB）
• GBM_Reservoir_5.zip（4.89 GB）
• GBM_Reservoir_6.zip（4.91 GB）
• GBM_Reservoir_7.zip（4.91 GB）
• GBM_Reservoir_8.zip（4.90 GB）
• GBM_Reservoir_9.zip（4.90 GB）
• GBM_Reservoir_10.zip（4.90 GB）
• GBM_Reservoir_11.zip（4.85 GB）
• GBM_Reservoir_12.zip（4.88 GB）
• GBM_Reservoir_13.zip（4.89 GB）
• GBM_Reservoir_14.zip（4.89 GB）
• GBM_Reservoir_15.zip（4.90 GB）
• GBM_Reservoir_16.zip（4.91 GB）
• GBM_Reservoir_17.zip（4.90 GB）
• GBM_Reservoir_18.zip（4.90 GB）

文件总体积 379.86 GB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

In this repository, we present a brain tumor database collection comprising 23,049 samples, with each sample including four different types of MRI brain scans: FLAIR, T1, T1ce, and T2. Additionally, one or two segmentation masks (ground truth) are provided for each sample. The first mask is the raw output from the registration process and is provided for all samples, while the second mask, provided particularly for synthetic samples, is a post-processed version of the first, designed to simplify interpretation and optimize it for network training. These samples have been acquired via registration process of 438 samples available at the moment of registration from the original dataset provided by the BraTS 2022 Challenge. Registering each pair of existing brain scans results in two additional scans that retain a similar brain shape while featuring varying tumor locations. Consequently, by registering all possible pairs, a dataset originally consisting of n samples can be expanded to n2 samples. The original dataset was collected from different institutions under standard clinical conditions, but with different equipment and imaging protocols. As a result, the image quality is heterogeneous, reflecting the diversity of clinical practices across institutions. This dataset can be utilized for various tasks, such as developing fully automated segmentation algorithms for new, unseen brain tumor cases, particularly through deep learning-based approaches, since ground truth is provided for each sample.

If you use this dataset in your research, please cite:
Solak, N., Ferreira, A., Luijten, G., Puladi, B., Alves, V., & Egger, J. (2025). GBM-Reservoir: Brain tumor (Glioblastoma Multiforme) MRI dataset collection with ground truth segmentation masks. Data in Brief. https://doi.org/10.1016/j.dib.2025.111287
Solak, N., Ferreira, A., Luijten, G., Puladi, B., Alves, V., & Egger, J. (2025). GBM-Reservoir: Dataset and Segmentations. Figshare. https://doi.org/10.6084/m9.figshare.28001450

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.28001450.v20（source: https://figshare.com/articles/dataset/GBM-Reservoir_Dataset_and_Segmentations/28001450）。