Table_1_深度学习赋能的中风CT智能化扫描临床适用性表格数据集

数据集获取（Figshare）

• 源站标题：Table_1_Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.DOCX
• DOI：10.3389/fneur.2022.755492.s001
• 发布日期：2022-03-11
• 文件数：1 个
• 文件总体积：3.98 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Deep_Learning-Enabled_Clinically_Applicable_CT_Planbox_for_Stroke_With_High_Accuracy_and_Repeatability_DOCX/19343384

命令行下载

curl -L -o "Table_1_Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.DOCX" "https://ndownloader.figshare.com/files/34351067"

文件清单

• Table_1_Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.DOCX（3.98 MB）

文件总体积 3.98 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

BackgroundComputed tomography (CT) plays an essential role in classifying stroke, quantifying penumbra size and supporting stroke-relevant radiomics studies. However, it is difficult to acquire standard, accurate and repeatable images during follow-up. Therefore, we invented an intelligent CT to evaluate stroke during the entire follow-up.
MethodsWe deployed a region proposal network (RPN) and V-Net to endow traditional CT with intelligence. Specifically, facial detection was accomplished by identifying adjacent jaw positions through training and testing an RPN on 76,382 human faces using a preinstalled 2-dimensional camera; two regions of interest (ROIs) were segmented by V-Net on another training set with 295 subjects, and the moving distance of scanning couch was calculated based on a pre-generated calibration table. Multiple cohorts including 1,124 patients were used for performance validation under three clinical scenarios.
ResultsCranial Automatic Planbox Imaging Towards AmeLiorating neuroscience (CAPITAL)-CT was invented. RPN model had an error distance of 4.46 ± 0.02 pixels with a success rate of 98.7% in the training set and 100% with 2.23 ± 0.10 pixels in the testing set. V-Net-derived segmentation maintained a clinically tolerable distance error, within 3 mm on average, and all lines presented with a tolerable angle error, within 3° on average in all boundaries. Real-time, accurate, and repeatable automatic scanning was accomplished with and a lower radiation exposure dose (all P CAPITAL-CT generated standard and reproducible images that could simplify the work of radiologists, which would be of great help in the follow-up of stroke patients and in multifield research in neuroscience.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2022.755492.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Deep_Learning-Enabled_Clinically_Applicable_CT_Planbox_for_Stroke_With_High_Accuracy_and_Repeatability_DOCX/19343384）。