Meply直肠周围转移淋巴结CT数据集

数据集获取

git clone https://github.com/kanydao/CoSAM.git

curl -L -o repo.zip https://github.com/kanydao/CoSAM/archive/refs/heads/main.zip
unzip repo.zip

源站 README 摘录（使用方式）

Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Segmentation

CoSAM Overview

The frame work of our proposed CoSAM. The code of CoSAM is provided in code/cosam.py.

Dataset Overview

The Metastatic Perirectal Lymph node dataset (MEPLY), encompassing 269 enhanced computed tomography (CT) scans with a voxel resolution of 0.625mm, is tailored for the specific task of lymph node segmentation. Annotating each scan meticulously, Meply offers invaluable data facilitating precise delineation of perirectal lymph nodes.

Dataset Example

We provide examples of our dataset in examples. Each npz file contains a dictionary with the following keys:

• img: a numpy array of shape (H, W, D) representing the CT sequence.
• label: a numpy array of shape (H, W, D) representing the ground truth lymph nodesegmentation mask.
• H = W = 512, D = 15
  An example of how to load the data and visualize it is given in lymph_dataloader.py.

Request for Access to the Dataset

If you are interested in our full dataset, please contact <EMAIL> wansh@ustc.edu.cn and provide your organization’'s information and the purpose of use.

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。

完整仓库：github.com/kanydao/CoSAM