A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence

Intracerebral hemorrhage (ICH) and perihematomal edema (PHE) are key imaging markers of primary and secondary brain injury in hemorrhagic stroke. Accurate segmentation and quantification of ICH and PHE can help with prognostication and guide treatment planning. In this study, we combined Swin-Unet T...

Full description

Saved in:

Bibliographic Details
Main Authors:	Anh T. Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Fiona Dierksen, Andrea Dell’Orco, Helge Kniep, Uta Hanning, Jens Fiehler, Julia Zietz, Pina C. Sanelli, Ajay Malhotra, James S. Duncan, Sanjay Aneja, Guido J. Falcone, Adnan I. Qureshi, Kevin N. Sheth, Jawed Nawabi, Seyedmehdi Payabvash
Format:	Article
Language:	English
Published:	MDPI AG 2024-12-01
Series:	Bioengineering
Subjects:	intracerebral hemorrhage perihematomal edema segmentation
Online Access:	https://www.mdpi.com/2306-5354/11/12/1274
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence

Similar Items