Automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injury-Reference-Cited by-同舟云学术

Automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injury

Published:2022 Issue: Volume: Page:135-146
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Rosnati Margherita,Soreq Eyal,Monteiro Miguel,Li Lucia,Graham Neil S. N.,Zimmerman Karl,Rossi Carlotta,Carrara Greta,Bertolini Guido,Sharp David J.,Glocker Ben

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-17899-3_14

Reference28 articles.

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2. Brown, A.W., et al.: Predictive utility of an adapted Marshall head CT classification scheme after traumatic brain injury. Brain Injury 33, 610–617 (2019)

3. Bruschetta, R., et al.: Predicting outcome of traumatic brain injury: is machine learning the best way? Biomedicines 10, 686 (2022)

4. Carter, E., Coles, J.P.: Imaging in the diagnosis and prognosis of traumatic brain injury. Expert Opinion Med. Diagnostics 6, 541–554 (2012)

5. Chaganti, S., Plassard, A.J., Wilson, L., Smith, M.A., Patel, M.B., Landman, B.A.: A Bayesian framework for early risk prediction in traumatic brain injury. In: Medical Imaging 2016: Image Processing, vol. 9784. International Society for Optics and Photonics (2016)

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2. Investigating the characteristics and correlates of systemic inflammation after traumatic brain injury: the TBI-BraINFLAMM study;BMJ Open;2023-05

3. Manual lesion segmentations for traumatic brain injury characterization;Frontiers in Neuroimaging;2023-03-16