Machine learning segmentation of core and penumbra from acute stroke CT perfusion data

Frontiers in Neurology

Neurology

Tim Kleinig¹  Longting Lin²  Christopher Levi²  Neil Spratt²  Freda Werdiger³  Milanka Visser³  Andrew Bivard³  Mark W. Parsons⁴ 

[1] Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia;Hunter Medical Research Institution, University of Newcastle, Newcastle, NSW, Australia;Department of Neurology, John Hunter Hospital, University of Newcastle, Newcastle, NSW, Australia;Melbourne Brain Centre, Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia;Department of Medicine, University of Melbourne, Melbourne, VIC, Australia;Southwestern Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia;Department of Neurology, Liverpool Hospital, Liverpool, NSW, Australia;Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia;

关键词: acute ischemic stroke;    CT perfusion imaging;    machine learning;    ischemic core;    penumbra;   

DOI  :  10.3389/fneur.2023.1098562

 received in 2022-11-20, accepted in 2023-02-02,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionComputed tomography perfusion (CTP) imaging is widely used in cases of suspected acute ischemic stroke to positively identify ischemia and assess suitability for treatment through identification of reversible and irreversible tissue injury. Traditionally, this has been done via setting single perfusion thresholds on two or four CTP parameter maps. We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously.MethodsWe used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images.ResultsXGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model demonstrated moderate agreement in segmenting core and penumbra on test images. Dice similarity coefficients for core and penumbra were 0.38 ± 0.26 and 0.50 ± 0.21, respectively, demonstrating moderate agreement. Skull-related image artefacts contributed to lower accuracy.DiscussionFurther development may enable us to move beyond the current overly simplistic core and penumbra definitions using single thresholds where a single error or artefact may lead to substantial error.

【 授权许可】

Unknown   
Copyright © 2023 Werdiger, Parsons, Visser, Levi, Spratt, Kleinig, Lin and Bivard.