TY - GEN
T1 - Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images
AU - Aghaei, Faranak
AU - Ross, Stephen R.
AU - Wang, Yunzhi
AU - Wu, Dee H.
AU - Cornwell, Benjamin O.
AU - Ray, Bappaditya
AU - Zheng, Bin
N1 - Publisher Copyright:
© 2017 SPIE.
PY - 2017
Y1 - 2017
N2 - Aneurysmal subarachnoid hemorrhage (aSAH) is a form of hemorrhagic stroke that affects middle-aged individuals and associated with significant morbidity and/or mortality especially those presenting with higher clinical and radiologic grades at the time of admission. Previous studies suggested that blood extravasated after aneurysmal rupture was a potentially clinical prognosis factor. But all such studies used qualitative scales to predict prognosis. The purpose of this study is to develop and test a new interactive computer-aided detection (CAD) tool to detect, segment and quantify brain hemorrhage and ventricular cerebrospinal fluid on non-contrasted brain CT images. First, CAD segments brain skull using a multilayer region growing algorithm with adaptively adjusted thresholds. Second, CAD assigns pixels inside the segmented brain region into one of three classes namely, normal brain tissue, blood and fluid. Third, to avoid "black-box" approach and increase accuracy in quantification of these two image markers using CT images with large noise variation in different cases, a graphic User Interface (GUI) was implemented and allows users to visually examine segmentation results. If a user likes to correct any errors (i.e., deleting clinically irrelevant blood or fluid regions, or fill in the holes inside the relevant blood or fluid regions), he/she can manually define the region and select a corresponding correction function. CAD will automatically perform correction and update the computed data. The new CAD tool is now being used in clinical and research settings to estimate various quantitatively radiological parameters/markers to determine radiological severity of aSAH at presentation and correlate the estimations with various homeostatic/metabolic derangements and predict clinical outcome.
AB - Aneurysmal subarachnoid hemorrhage (aSAH) is a form of hemorrhagic stroke that affects middle-aged individuals and associated with significant morbidity and/or mortality especially those presenting with higher clinical and radiologic grades at the time of admission. Previous studies suggested that blood extravasated after aneurysmal rupture was a potentially clinical prognosis factor. But all such studies used qualitative scales to predict prognosis. The purpose of this study is to develop and test a new interactive computer-aided detection (CAD) tool to detect, segment and quantify brain hemorrhage and ventricular cerebrospinal fluid on non-contrasted brain CT images. First, CAD segments brain skull using a multilayer region growing algorithm with adaptively adjusted thresholds. Second, CAD assigns pixels inside the segmented brain region into one of three classes namely, normal brain tissue, blood and fluid. Third, to avoid "black-box" approach and increase accuracy in quantification of these two image markers using CT images with large noise variation in different cases, a graphic User Interface (GUI) was implemented and allows users to visually examine segmentation results. If a user likes to correct any errors (i.e., deleting clinically irrelevant blood or fluid regions, or fill in the holes inside the relevant blood or fluid regions), he/she can manually define the region and select a corresponding correction function. CAD will automatically perform correction and update the computed data. The new CAD tool is now being used in clinical and research settings to estimate various quantitatively radiological parameters/markers to determine radiological severity of aSAH at presentation and correlate the estimations with various homeostatic/metabolic derangements and predict clinical outcome.
KW - Aneurysmal Subarachnoid Hemorrhage
KW - Brain CT imaging
KW - Imaging informatics for predicting disease prognosis
KW - Interactive computer-aided detection
KW - Quantitative of radiologic markers
UR - http://www.scopus.com/inward/record.url?scp=85020485603&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020485603&partnerID=8YFLogxK
U2 - 10.1117/12.2254094
DO - 10.1117/12.2254094
M3 - Conference contribution
AN - SCOPUS:85020485603
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2017
A2 - Cook, Tessa S.
A2 - Zhang, Jianguo
PB - SPIE
T2 - Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Y2 - 15 February 2017 through 16 February 2017
ER -