图4数据集
包含扩散受限成像中ADC减法与扩散峰度成像对比数据,用于脑胶质瘤及脑血管梗塞诊断评估。

基本信息
资源简介
图4数据集
【数据集背景】
本数据集源站标题为《Dataset for Fig 4.》。源站首次发布:2023-02-27。
【数据内容】
源站原始描述:A number of restricted diffusion (RD) imaging techniques, such as diffusion kurtosis (DK) imaging and Q space imaging, have been developed and proven to be useful for the diagnosis of diseases, including cerebral gliomas and cerebrovascular infarction. In particular, apparent diffusion coefficient (ADC) subtraction method (ASM) imaging has become available recently as a novel RD imaging technique. ASM is based on the difference between the ADC values in an image pair of two ADC maps, ADC basic (…
【数据结构与技术规格】
文件数 1 个,合计 72.04 KB。pone.0282462.s002.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet,72.04 KB。数据集 DOI:10.1371/journal.pone.0282462.s002。
【主题与分类】
源站学科分类:Biophysics、Biochemistry、Space Science、Medicine、Microbiology、Cell Biology。主题标签:present study aimed、including cerebral gliomas、future clinical applications、become available recently、div >< p、q space imaging、sensitive images compared、,、),、two adc maps。
【适用方向】
影像类数据可用于医学图像分析模型(检测、分割、分类)的训练与评测,也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面:https://plos.figshare.com/articles/dataset/Dataset_for_Fig_4_/22185018
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Barham, Majd; Kuroda, Masahiro; Yoshimura, Yuuki; Hamada, Kentaro; Khasawneh, Abdullah; Sugimoto, Kohei; et al. (2023). Dataset for Fig 4.. PLOS ONE. Dataset
【使用注意】
· 源站最后修改于 2023-05-30,引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取(Figshare)
- 源站标题:Dataset for Fig 4.
- DOI:10.1371/journal.pone.0282462.s002
- 发布日期:2023-02-27
- 文件数:1 个
- 文件总体积:72.04 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Dataset_for_Fig_4_/22185018
命令行下载
curl -L -o "pone.0282462.s002.xlsx" "https://ndownloader.figshare.com/files/39424005"
文件清单
pone.0282462.s002.xlsx(72.04 KB)
文件总体积 72.04 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
A number of restricted diffusion (RD) imaging techniques, such as diffusion kurtosis (DK) imaging and Q space imaging, have been developed and proven to be useful for the diagnosis of diseases, including cerebral gliomas and cerebrovascular infarction. In particular, apparent diffusion coefficient (ADC) subtraction method (ASM) imaging has become available recently as a novel RD imaging technique. ASM is based on the difference between the ADC values in an image pair of two ADC maps, ADC basic (ADCb) and ADC modify (ADCm), which are created from diffusion-weighted images taken using short and long effective diffusion times, respectively. The present study aimed to assess the potential of different types of ASM imaging by comparing them with DK imaging which is the gold-standard RD imaging technique. In the present basic study using both polyethylene glycol phantom and cell-containing bio-phantom, three different types of ASM images were created using different calculation processes. ASM/A is an image calculated by dividing the absolute difference between ADCb and ADCm by ADCb several times. By contrast, ASM/S is an image created by dividing the absolute difference between ADCb and ADCm by the standard deviation of ADCb several times. As for positive ASM/A image (PASM/A), the positive image, which was resultant after subtracting ADCb from ADCm, was divided by ADCb several times. A comparison was made between the types of ASM and DK images. The results showed the same tendency between ASM/A in addition to both ASM/S and PASM/A. By increasing the number of divisions by ADCb from three to five times, ASM/A images transformed from DK-mimicking to more RD-sensitive images compared with DK images. These observations suggest that ASM/A images may prove useful for future clinical applications in RD imaging protocols for the diagnosis of diseases.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0282462.s002(source: https://plos.figshare.com/articles/dataset/Dataset_for_Fig_4_/22185018)。
数据缺失?
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