Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography

Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography

Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in la...

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Bibliographic Details
Main Authors: Anh T. Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Julia Zietz, Andrea Dell’Orco, Min-Chiun Chen, Ajay Malhotra, Adnan I. Qureshi, Santosh B. Murthy, Shahram Majidi, Guido J. Falcone, Kevin N. Sheth, Jawed Nawabi, Seyedmehdi Payabvash
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
intracerebral hemorrhage
hematoma expansion
convolution neural network
segmentation
classification
ground truth generation
Online Access:https://www.mdpi.com/2076-3417/15/1/111
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