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Nowinski WL. Taxonomy of Acute Stroke: Imaging, Processing, and Treatment. Diagnostics (Basel) 2024; 14:1057. [PMID: 38786355 PMCID: PMC11119045 DOI: 10.3390/diagnostics14101057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/01/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.
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Affiliation(s)
- Wieslaw L Nowinski
- Sano Centre for Computational Personalised Medicine, Czarnowiejska 36, 30-054 Krakow, Poland
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Ostmeier S, Axelrod B, Liu Y, Yu Y, Jiang B, Yuen N, Pulli B, Verhaaren BFJ, Kaka H, Wintermark M, Michel P, Mahammedi A, Federau C, Lansberg MG, Albers GW, Moseley ME, Zaharchuk G, Heit JJ. Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT. J Neurointerv Surg 2024:jnis-2023-021283. [PMID: 38302420 DOI: 10.1136/jnis-2023-021283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 01/15/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations. METHODS The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial. Additional external validation was performed using 33 patients with matched stroke onset times from the University Hospital Lausanne. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. The median of volume, overlap, and distance segmentation metrics were determined for agreement in lesion segmentations between (1) three experts, (2) the majority model and each expert, and (3) the random model and each expert. The two sided Wilcoxon signed rank test was used to compare performances (1) to 2) and (1) to (3). We further compared volumes with the 24 hour follow-up diffusion weighted imaging (DWI, final infarct core) and correlations with clinical outcome (modified Rankin Scale (mRS) at 90 days) with the Spearman method. RESULTS The random model outperformed the inter-expert agreement ((1) to (2)) and the majority model ((1) to (3)) (dice 0.51±0.04 vs 0.36±0.05 (P<0.0001) vs 0.45±0.05 (P<0.0001)). The random model predicted volume correlated with clinical outcome (0.19, P<0.05), whereas the median expert volume and majority model volume did not. There was no significant difference when comparing the volume correlations between random model, median expert volume, and majority model to 24 hour follow-up DWI volume (P>0.05, n=51). CONCLUSION The random model for ischemic injury delineation on non-contrast CT surpassed the inter-expert agreement ((1) to (2)) and the performance of the majority model ((1) to (3)). We showed that the random model volumetric measures of the model were consistent with 24 hour follow-up DWI.
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Affiliation(s)
- Sophie Ostmeier
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Yongkai Liu
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Yannan Yu
- Department of Radiology, University of California San Francisco, San Francisco, California, USA
| | - Bin Jiang
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Nicole Yuen
- Department of Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Benjamin Pulli
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Hussam Kaka
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Max Wintermark
- Department of Radiology, University of Virginia, Charlottesville, Virginia, USA
| | - Patrik Michel
- Department of Neurology Service, University of Lausanne, Lausanne, Switzerland
| | | | | | - Maarten G Lansberg
- Department of Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Gregory W Albers
- Department of Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Michael E Moseley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Gregory Zaharchuk
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jeremy J Heit
- Department of Radiology, Neuroadiology and Neurointervention Division, Stanford University School of Medicine, Palo Alto, CA, USA
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Dogan S, Barua PD, Baygin M, Chakraborty S, Ciaccio E, Tuncer T, Abd Kadir KA, Md Shah MN, Azman RR, Lee CC, Ng KH, Acharya UR. Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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