Evaluation of an Automatic ASPECT Scoring System for Acute Stroke in Non-Contrast CT

Matt Daykin*, Erin Beveridge, Vismantas Dilys, Aneta Lisowska, Keith Muir, Mathini Sellathurai, Ian Poole

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Determining the severity of ischemic stroke in non-contrast CT is a difficult problem due to a low signal to noise ratio. This leads to variable interpretation of ischemic stroke severity. We investigate the level of agreement between four methods including the use of an automated system with the aim of identifying early ischemic changes within the brain. For the evaluation we divide the middle cerebral artery territory of each hemisphere into ten regions defined according to the Alberta Stroke Programme Early CT Score (ASPECTS). The automatic system uses a specialised Convolutional Neural Network (CNN) based regressor to produce voxel-level confidence masks of which voxels are suspected as showing early ischemic change and from this we compute the score. Additionally, we obtain the score from three other methods that involved trained human graders. We compare the level of agreement between these methods at both a patient level and a territory level through Simultaneous Truth and Performance Level Estimation (STAPLE) and Cohen’s kappa coefficient. We analyse possible causes of disagreement between the methods and statistically validate the performance of the CNN model against the performance of clinical staff. We find that the CNN produces scores that correlate the greatest with its training data at the patient level, but the training data could be improved to strengthen the correlation with the professional standard.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
EditorsMaría Valdés Hernández, Víctor González-Castro
Number of pages11
ISBN (Electronic)9783319609645
ISBN (Print)9783319609638
Publication statusPublished - 22 Jun 2017
Event21st Annual Conference on Medical Image Understanding and Analysis 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference21st Annual Conference on Medical Image Understanding and Analysis 2017
Abbreviated titleMIUA 2017
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • General Computer Science


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