Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study

Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study

Key points The model simultaneously segments infarcts and estimates ASPECTS by using baseline CT. A mirror-assembly module plus dual-path DCNN model improved the segmentation efficiency. We evaluated the model in a multi-reader and multicenter (MRMC) setting.

Bibliographic Details
Main Authors: Weidao Chen, Jiangfen Wu, Ren Wei, Shuang Wu, Chen Xia, Dawei Wang, Daliang Liu, Longmei Zheng, Tianyu Zou, Ruijiang Li, Xianrong Qi, Xiaotong Zhang
Format: Article
Language:English
Published: SpringerOpen 2022-12-01
Series:Insights into Imaging
Subjects:
Deep learning
Non-contrast computed tomography
Early ischemic changes
Alberta Stroke Program Early Computed Tomography Score
Multi-reader and multi-center study
Acute ischemic stroke
Online Access:https://doi.org/10.1186/s13244-022-01331-3

Internet

https://doi.org/10.1186/s13244-022-01331-3

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