VQ4PEDB：肺栓塞AI辅助标注大规模肺通气灌注显像数据库

数据集获取（Figshare）

• 源站标题：Table 1_On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PEDB).docx
• DOI：10.3389/fnume.2025.1632112.s001
• 发布日期：2025-07-17
• 文件数：1 个
• 文件总体积：18.45 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_On_the_construction_of_a_large-scale_database_of_AI-assisted_annotating_lung_ventilation-perfusion_scintigraphy_for_pulmonary_embolism_VQ4PEDB_docx/29588165

命令行下载

curl -L -o "Table 1_On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PED.docx" "https://ndownloader.figshare.com/files/56336633"

文件清单

• Table 1_On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PED.docx（18.45 KB）

文件总体积 18.45 KB（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

IntroductionVentilation-perfusion (V/Q) nuclear scintigraphy remains a vital diagnostic tool for assessing pulmonary embolism (PE) and other lung conditions. Interpretation of these images requires specific expertise which may benefit from recent advances in artificial intelligence (AI) to improve diagnostic accuracy and confidence in reporting. Our study aims to develop a multi-center dataset combining imaging and clinical reports to aid in creating AI models for PE diagnosis.
MethodsWe established a comprehensive imaging registry encompassing patient-level V/Q image data along with relevant clinical reports, CTPA images, DVT ultrasound impressions, D-dimer lab tests, and thrombosis unit records. Data extraction was performed at two hospitals in Canada and at multiple sites in the United States, followed by a rigorous de-identification process. We utilized the V7 Darwin platform for crowdsourced annotation of V/Q images including segmentation of V/Q mismatched vascular defects. The annotated data was then ingested into Deep Lake, a SQL-based database, for AI model training. Quality assurance involved manual inspections and algorithmic validation.
ResultsA query of The Ottawa Hospital’'s data warehouse followed by initial data screening yielded 2,137 V/Q studies with 2,238 successfully retrieved as DICOM studies. Additional contributions included 600 studies from University Health Toronto, and 385 studies by private company Segmed Inc. resulting in a total of 3,122 V/Q planar and SPECT images. The majority of studies were acquired using Siemens, Philips, and GE scanners, adhering to standardized local imaging protocols. After annotating 1,500 studies from The Ottawa Hospital, the analysis identified 138 high-probability, 168 intermediate-probability, 266 low-probability, 244 very low-probability, and 669 normal, and 15 normal perfusion with reversed mismatched ventilation defect studies. In 1,500 patients were 3,511 segmented vascular perfusion defects.
ConclusionThe VQ4PEDB comprised 8 unique ventilation agents and 11 unique scanners. The VQ4PEDB database is unique in its depth and breadth in the domain of V/Q nuclear scintigraphy for PE, comprising clinical rep

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnume.2025.1632112.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_On_the_construction_of_a_large-scale_database_of_AI-assisted_annotating_lung_ventilation-perfusion_scintigraphy_for_pulmonary_embolism_VQ4PEDB_docx/29588165）。