ENDOTEST

数据集获取（Figshare）

• 源站标题：A video based benchmark data set (ENDOTEST) to evaluate computer-aided polyp detection systems
• DOI：10.6084/m9.figshare.20069835.v1
• 发布日期：2022-06-15
• 文件数：2 个
• 文件总体积：9.00 MB
• 源站页面：https://tandf.figshare.com/articles/dataset/A_video_based_benchmark_data_set_ENDOTEST_to_evaluate_computer-aided_polyp_detection_systems/20069835

命令行下载

curl -L -o "igas_a_2085059_sm3433.docx" "https://ndownloader.figshare.com/files/35902032"
curl -L -o "igas_a_2085059_sm3431.mp4" "https://ndownloader.figshare.com/files/35902035"

文件清单

• igas_a_2085059_sm3433.docx（22.40 KB）
• igas_a_2085059_sm3431.mp4（8.98 MB）

文件总体积 9.00 MB（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

Computer-aided polyp detection (CADe) may become a standard for polyp detection during colonoscopy. Several systems are already commercially available. We report on a video-based benchmark technique for the first preclinical assessment of such systems before comparative randomized trials are to be undertaken. Additionally, we compare a commercially available CADe system with our newly developed one.
ENDOTEST consisted in the combination of two datasets. The validation dataset contained 48 video-snippets with 22,856 manually annotated images of which 53.2% contained polyps. The performance dataset contained 10 full-length screening colonoscopies with 230,898 manually annotated images of which 15.8% contained a polyp. Assessment parameters were accuracy for polyp detection and time delay to first polyp detection after polyp appearance (FDT). Two CADe systems were assessed: a commercial CADe system (GI-Genius, Medtronic), and a self-developed new system (ENDOMIND). The latter being a convolutional neuronal network trained on 194,983 manually labeled images extracted from colonoscopy videos recorded in mainly six different gastroenterologic practices.
On the ENDOTEST, both CADe systems detected all polyps in at least one image. The per-frame sensitivity and specificity in full colonoscopies was 48.1% and 93.7%, respectively for GI-Genius; and 54% and 92.7%, respectively for ENDOMIND. Median FDT of ENDOMIND with 217 ms (Inter-Quartile Range(IQR)8–1533) was significantly faster than GI-Genius with 1050 ms (IQR 358–2767, p = 0.003).
Our benchmark ENDOTEST may be helpful for preclinical testing of new CADe devices. There seems to be a correlation between a shorter FDT with a higher sensitivity and a lower specificity for polyp detection.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.20069835.v1（source: https://tandf.figshare.com/articles/dataset/A_video_based_benchmark_data_set_ENDOTEST_to_evaluate_computer-aided_polyp_detection_systems/20069835）。