APIS
首个公开的急性缺血性中风NCCT与ADC配对医学影像数据集,用于中风病灶分割。

基本信息
资源简介
APIS
【数据集背景】
本数据集源站标题为《arXiv Query: search_query=&id_list=2309.15243v1&start=0&max_results=1》。源站首次发布:2023-09-26。
【数据内容】
源站原始描述:Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in localizing and delineating brain lesions. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. However, non-contrast CTs may lack sensitivity in detecting subtle ischemic changes in the acute phase. As a result, complementary diffus…
【数据结构与技术规格】
数据集 DOI:10.1038/s41598-024-71273-x。
【主题与分类】
主题标签:cs.CV、eess.IV、q-bio.NC。
【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://arxiv.org/abs/2309.15243v1
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
数据集说明
APIS 对应论文数据集(arXiv 预印本)。
数据获取指引
- 打开论文页面获取作者与项目信息:https://arxiv.org/abs/2309.15243v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置;
- 获取到实际数据链接后,按对应平台标准方式下载。
论文摘要:Abstract:Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in localizing and delineating brain lesions. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. However, non-contrast CTs may lack sensitivity in detecting subtle ischemic changes in the acute phase. As a result, complementary diffusion-weighted MRI studies are captured to provide valuable insights, allowing to recover and quantify stroke lesions. This work introduced APIS, the first paired public dataset with NCCT and ADC studies of acute ischemic stroke patients. APIS was presented as a challenge at the 20th IEEE International Symposium on Biomedical Imaging 2023, where researchers were invited to propose new computational strategies that leverage paired data and deal with lesion segmentation over CT sequences. Despite all the teams employing specialized deep learning tools, the results suggest that the ischemic stroke segmentation task from NCCT remains challenging. The annotated dataset remains accessible to the public upon registration, inviting the scientific community to deal with stroke characterization from NCCT but guided with paired DWI information.
数据缺失?
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