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Werdiger F, Parsons MW, Visser M, Levi C, Spratt N, Kleinig T, Lin L, Bivard A. Machine learning segmentation of core and penumbra from acute stroke CT perfusion data. Front Neurol 2023; 14:1098562. [PMID: 36908587 PMCID: PMC9995438 DOI: 10.3389/fneur.2023.1098562] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 02/02/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction Computed 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. Methods We 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. Results XGBoost 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. Discussion Further 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.
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Affiliation(s)
- Freda Werdiger
- Melbourne Brain Centre, Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia.,Department of Medicine, University of Melbourne, Melbourne, VIC, Australia
| | - Mark W Parsons
- 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
| | - Milanka Visser
- Melbourne Brain Centre, Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia.,Department of Medicine, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher Levi
- Hunter Medical Research Institution, University of Newcastle, Newcastle, NSW, Australia.,Department of Neurology, John Hunter Hospital, University of Newcastle, Newcastle, NSW, Australia
| | - Neil Spratt
- Hunter Medical Research Institution, University of Newcastle, Newcastle, NSW, Australia.,Department of Neurology, John Hunter Hospital, University of Newcastle, Newcastle, NSW, Australia
| | - Tim Kleinig
- Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Longting Lin
- Hunter Medical Research Institution, University of Newcastle, Newcastle, NSW, Australia.,Department of Neurology, John Hunter Hospital, University of Newcastle, Newcastle, NSW, Australia
| | - Andrew Bivard
- Melbourne Brain Centre, Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia.,Department of Medicine, University of Melbourne, Melbourne, VIC, Australia
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Chiu CH, Leu JD, Lin TT, Su PH, Li WC, Lee YJ, Cheng DC. Systematic Quantification of Cell Confluence in Human Normal Oral Fibroblasts. APPLIED SCIENCES 2020; 10:9146. [DOI: 10.3390/app10249146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
Background: The accurate determination of cell confluence is a critical step for generating reasonable results of designed experiments in cell biological studies. However, the cell confluence of the same culture may be diversely predicted by individual researchers. Herein, we designed a systematic quantification scheme implemented on the Matlab platform, the so-called “Confluence-Viewer” program, to assist cell biologists to better determine the cell confluence. Methods: Human normal oral fibroblasts (hOFs) seeded in 10 cm culture dishes were visualized under an inverted microscope for the acquisition of cell images. The images were subjected to the cell segmentation algorithm with top-hat transformation and the Otsu thresholding technique. A regression model was built using a quadratic model and shape-preserving piecewise cubic model. Results: The cell segmentation algorithm generated a regression curve that was highly correlated with the cell confluence determined by experienced researchers. However, the correlation was low when compared to the cell confluence determined by novice students. Interestingly, the cell confluence determined by experienced researchers became more diverse when they checked the same images without a time limitation (up to 1 min). Conclusion: This tool could prevent unnecessary human-made mistakes and meaningless repeats for novice researchers working on cell-based studies in health care or cancer research.
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