Multivariate Analysis of Ischaemic Lesions Using Computed Tomography and CT Perfusion Imaging: Critical Review

References

• Banerjee TK, Das SK. 2016. Fifty years of stroke researches in India. Ann Indian Acad Neurol. 19(1):1. doi: 10.4103/0972-2327.168631.
   PubMed Web of Science ®Google Scholar
• Barros RS, Tolhuisen ML, Boers AM, Jansen I, Ponomareva E, Dippel DW, van der Lugt A, van Oostenbrugge RJ, van Zwam WH, Berkhemer OA, et al. 2020. Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks. J NeuroIntervent Surg. 12(9):848–852. doi: 10.1136/neurintsurg-2019-015471.
   PubMed Web of Science ®Google Scholar
• Clerigues A, Valverde S, Bernal J, Freixenet J, Oliver A, Lladó X. 2019. Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Comput Biol Med. 115:103487. doi:10.1016/j.compbiomed.2019.103487.
   PubMed Web of Science ®Google Scholar
• Dobshik AV, Verbitskiy SK, Pestunov IA, Sherman KM, Sinyavskiy YN, Tulupov AA, Berikov VB. 2023. Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks. arXiv preprint arXiv:2301.06793.
   Google Scholar
• Dolz J, Ben Ayed I, Desrosiers C, 2019. Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, Revised Selected Papers, Part I 4 271–282. Springer International Publishing.
   Google Scholar
• Flottmann F, Broocks G, Faizy TD, Ernst M, Forkert ND, Grosser M, Thomalla G, Siemonsen S, Fiehler J, Kemmling A. 2017. CT-perfusion stroke imaging: a threshold free probabilistic approach to predict infarct volume compared to traditional ischemic thresholds. Sci Rep. 7(1):1–0. doi: 10.1038/s41598-017-06882-w.
   PubMedGoogle Scholar
• Gomolka RS, Chrzan RM, Urbanik A, Nowinski WL. 2016. A quantitative method using head noncontrast CT scans to detect hyperacute nonvisible ischemic changes in patients with stroke. J Neuroimaging. 26(6):581–587. doi: 10.1111/jon.12363.
   PubMed Web of Science ®Google Scholar
• Hachem M, Nacir H. 2022. Emerging role of phospholipids and lysophospholipids for improving brain docosahexaenoic acid as potential preventive and therapeutic strategies for neurological diseases. Int J Mol Sci. 23(7):3969. doi: 10.3390/ijms23073969.
   PubMed Web of Science ®Google Scholar
• Hedna VS, Shukla PP, Waters MF. 2012. Seizure mimicking stroke: role of CT perfusion. J Clin Imaging Sci. 2:32. doi:10.4103/2156-7514.97728.
   PubMedGoogle Scholar
• Kanchana R, Menaka R. 2017. A novel approach for characterisation of ischaemic stroke lesion using histogram bin-based segmentation and gray level co-occurrence matrix features. Imag Sci J. 65(2):124–136. doi: 10.1080/13682199.2017.1295586.
   Web of Science ®Google Scholar
• Kanchana R, Menaka R. 2020. Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection. Biomed Eng Lett. 10(3):333–344. doi: 10.1007/s13534-020-00158-5.
   PubMed Web of Science ®Google Scholar
• Katan M, Luft A. 2018. Global burden of stroke. Semin Neurol. 38(2):208–211. doi: 10.1055/s-0038-1649503.
   PubMed Web of Science ®Google Scholar
• Kemmling A, Flottmann F, Forkert ND, Minnerup J, Heindel W, Thomalla G, Eckert B, Knauth M, Psychogios M, Langner S, et al. 2015. Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization. J Cereb Blood Flow Metab. 35(9):1397–1405. doi: 10.1038/jcbfm.2015.144.
   PubMed Web of Science ®Google Scholar
• Kidwell CS, Wintermark M, De Silva DA, Schaewe TJ, Jahan R, Starkman S, Jovin T, Hom J, Jumaa M, Schreier J, et al. 2013. Multiparametric MRI and CT models of infarct core and favorable penumbral imaging patterns in acute ischemic stroke. Stroke. 44(1):73–79. doi: 10.1161/STROKEAHA.112.670034.
   PubMed Web of Science ®Google Scholar
• Kuang H, Menon BK, Qiu W. 2019. Semi‐automated infarct segmentation from follow‐up noncontrast CT scans in patients with acute ischemic stroke. Med Phys. 46(9):4037–4045. doi: 10.1002/mp.13703.
   PubMed Web of Science ®Google Scholar
• Liu L, Yang S, Meng L, Li M, Wang J 2019. Multi-scale deep convolutional neural network for stroke lesions segmentation on CT images. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, Granada, Spain. Springer International Publishing. pp. 283–291.
   Google Scholar
• Mäkelä T, Öman O, Hokkinen L, Wilppu U, Salli E, Savolainen S, Kangasniemi M. 2022. Automatic CT Angiography lesion segmentation compared to CT perfusion in ischemic stroke detection: a feasibility study. J Digit Imaging. 35(3):551–563. doi: 10.1007/s10278-022-00611-0.
   PubMed Web of Science ®Google Scholar
• Maldjian JA, Chalela J, Kasner SE, Liebeskind D, Detre JA. 2001. Automated CT segmentation and analysis for acute middle cerebral artery stroke. AJNR Am J Neuroradiol. 22(6):1050–1055.
   PubMed Web of Science ®Google Scholar
• Nael K, Tadayon E, Wheelwright D, Metry A, Fifi JT, Tuhrim S, De Leacy RA, Doshi AH, Chang HL, Mocco J. 2019. Defining ischemic core in acute ischemic stroke using CT perfusion: a multiparametric bayesian-based model. AJNR Am J Neuroradiol. 40(9):1491–1497. doi: 10.3174/ajnr.A6170.
   PubMed Web of Science ®Google Scholar
• Nowinski WL. 2020. Human brain atlases in stroke management. Neuroinformatics. 18(4):549–567. doi: 10.1007/s12021-020-09462-y.
   PubMed Web of Science ®Google Scholar
• Nowinski WL, Gupta V, Qian G, He J, Poh LE, Ambrosius W, Chrzan RM, Polonara G, Mazzoni C, Mol M, et al. 2013. Automatic detection, localization, and volume estimation of ischemic infarcts in noncontrast computed tomographic scans: method and preliminary results. Invest Radiol. 48(9):661–670. doi: 10.1097/RLI.0b013e31828d8403.
   PubMed Web of Science ®Google Scholar
• Nowinski WL, Walecki J, Półtorak-Szymczak G, Sklinda K, Mruk B. 2020. Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods. PeerJ. 8:e10444. doi: 10.7717/peerj.10444.
   PubMed Web of Science ®Google Scholar
• Pan J, Wu G, Yu J, Geng D, Zhang J, Wang Y. 2021. Detecting the early infarct core on non-contrast CT images with a deep learning residual network. J Stroke Cerebrovasc. 30(6):105752. doi: 10.1016/j.jstrokecerebrovasdis.2021.105752.
   PubMed Web of Science ®Google Scholar
• Poh LE, Gupta V, Johnson A, Kazmierski R, Nowinski WL. 2012. Automatic segmentation of ventricular cerebrospinal fluid from ischemic stroke CT images. Neuroinformatics. 10(2):159–172. doi: 10.1007/s12021-011-9135-9.
   PubMed Web of Science ®Google Scholar
• Qiu W, Kuang H, Teleg E, Ospel JM, Sohn SI, Almekhlafi M, Goyal M, Hill MD, Demchuk AM, Menon BK. Machine learning for detecting early infarction in acute stroke with non–contrast-enhanced CT. Radiology. 2020 Mar;294(3):638–644. doi: 10.1148/radiol.2020191193
   PubMed Web of Science ®Google Scholar
• Ray A, Bandyopadhyay SK. Automatic detection of ischemic stroke lesion using textural analysis from brain CT images. Eur J Biomed Pharm Sci. 2016;10:82–288.
   Google Scholar
• Rekik I, Allassonnière S, Carpenter TK, Wardlaw JM. 2012. Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. NeuroImage. 1(1):164–178. doi: 10.1016/j.nicl.2012.10.003.
   PubMed Web of Science ®Google Scholar
• Sahoo PK, Mohapatra S, Wu CY, Huang KL, Chang TY, Lee TH. 2022. Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach. Sci Rep. 12(1):18054. doi: 10.1038/s41598-022-22939-x.
   PubMed Web of Science ®Google Scholar
• Sangole TR, Vaze V, Gaikwad A. 2022. Study of patchmatch based tree-seed fuzzy clustering for ischemic stroke lesion. Int J Innov Eng Res Technol. 7:139–148.
   Google Scholar
• Soltanpour M, Greiner R, Boulanger P, Buck B 2019. Ischemic stroke lesion prediction in ct perfusion scans using multiple parallel u-nets following by a pixel-level classifier. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece. IEEE. pp. 957–963.
   Google Scholar
• Soltanpour M, Greiner R, Boulanger P, Buck B. 2021. Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network. Comput Biol Med. 137:104849. doi: 10.1016/j.compbiomed.2021.104849.
   PubMed Web of Science ®Google Scholar
• Tang FH, Ng DK, Chow DH. 2011. An image feature approach for computer-aided detection of ischemic stroke. Comput Biol Med. 41(7):529–536. doi: 10.1016/j.compbiomed.2011.05.001.
   PubMed Web of Science ®Google Scholar
• Tyan YS, Wu MC, Chin CL, Kuo YL, Lee MS, Chang HY. 2014. Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method. Int J Biomed Imaging. 2014:1–12. doi: 10.1155/2014/947539.
   Google Scholar
• Vos PC, Biesbroek JM, Weaver NA, Velthuis BK, Viergever MA. 2013. Automatic detection and segmentation of ischemic lesions in computed tomography images of stroke patients. In: Medical imaging 2013: computer-aided diagnosis. Vol. 8670. Florida, United States: SPIE; pp. 283–288.
   Google Scholar
• Wang G, Song T, Dong Q, Cui M, Huang N, Zhang S. 2020. Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med Image Anal. 65:101787. doi: 10.1016/j.media.2020.101787.
   PubMed Web of Science ®Google Scholar
• Wilke M, de Haan B, Juenger H, Karnath HO 2011. Semi-automated, and automated delineation of chronic brain lesions: a comparison of methods. NeuroImage. 56(4):2038–2046.
   Google Scholar
• Yu Z, Chen Z, Yu Y, Zhu H, Tong D, Chen Y. 2021. An automated ASPECTS method with atlas-based segmentation. Comput Methods Programs Biomed. 210:106376. doi: 10.1016/j.cmpb.2021.106376.
   PubMed Web of Science ®Google Scholar