TY - JOUR
T1 - Automated brain computed tomographic densitometry of early ischemic changes in acute stroke
AU - Stoel, Berend C.
AU - Marquering, Henk A.
AU - Staring, Marius
AU - Beenen, Ludo F.
AU - Slump, Cornelis H.
AU - Roos, Yvo B.
AU - Majoie, Charles B.
PY - 2015
Y1 - 2015
N2 - The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS (R = -0.399 and -0.277, respectively) and with follow-up mRS (R = -0.256 and -0.272), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences <1 point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring. (c) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI
AB - The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS (R = -0.399 and -0.277, respectively) and with follow-up mRS (R = -0.256 and -0.272), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences <1 point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring. (c) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI
U2 - https://doi.org/10.1117/1.JMI.2.1.014004
DO - https://doi.org/10.1117/1.JMI.2.1.014004
M3 - Article
C2 - 26158082
SN - 2329-4302
VL - 2
SP - 014004
JO - Journal of medical imaging (Bellingham, Wash.)
JF - Journal of medical imaging (Bellingham, Wash.)
IS - 1
ER -