Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images

Faranak Aghaei, Stephen R. Ross, Yunzhi Wang, Dee H. Wu, Benjamin O. Cornwell, Bappaditya Ray, Bin Zheng

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

4 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsTessa S. Cook, Jianguo Zhang
PublisherSPIE
ISBN (Electronic)9781510607217
DOIs
StatePublished - Jan 1 2017
Externally publishedYes
EventMedical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications - Orlando, United States
Duration: Feb 15 2017Feb 16 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10138
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
CountryUnited States
CityOrlando
Period2/15/172/16/17

Fingerprint

markers
brain
Brain
hemorrhages
blood
Blood
prognosis
Subarachnoid Hemorrhage
Fluids
fluids
cerebrospinal fluid
Cerebrospinal fluid
skull
Intracranial Hemorrhages
mortality
strokes
Skull
User interfaces
Cerebrospinal Fluid
boxes

Keywords

  • Aneurysmal Subarachnoid Hemorrhage
  • Brain CT imaging
  • Imaging informatics for predicting disease prognosis
  • Interactive computer-aided detection
  • Quantitative of radiologic markers

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Aghaei, F., Ross, S. R., Wang, Y., Wu, D. H., Cornwell, B. O., Ray, B., & Zheng, B. (2017). Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images. In T. S. Cook, & J. Zhang (Eds.), Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications [1013805] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10138). SPIE. https://doi.org/10.1117/12.2254094

Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images. / Aghaei, Faranak; Ross, Stephen R.; Wang, Yunzhi; Wu, Dee H.; Cornwell, Benjamin O.; Ray, Bappaditya; Zheng, Bin.

Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. ed. / Tessa S. Cook; Jianguo Zhang. SPIE, 2017. 1013805 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10138).

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

Aghaei, F, Ross, SR, Wang, Y, Wu, DH, Cornwell, BO, Ray, B & Zheng, B 2017, Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images. in TS Cook & J Zhang (eds), Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications., 1013805, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10138, SPIE, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, Orlando, United States, 2/15/17. https://doi.org/10.1117/12.2254094
Aghaei F, Ross SR, Wang Y, Wu DH, Cornwell BO, Ray B et al. Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images. In Cook TS, Zhang J, editors, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. SPIE. 2017. 1013805. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2254094
Aghaei, Faranak ; Ross, Stephen R. ; Wang, Yunzhi ; Wu, Dee H. ; Cornwell, Benjamin O. ; Ray, Bappaditya ; Zheng, Bin. / Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images. Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. editor / Tessa S. Cook ; Jianguo Zhang. SPIE, 2017. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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