Multi-view iterative random walker for automated salvageable tissue delineation in ischemic stroke from multi-sequence MRI

Fisher, 1997, Characterizing the target of acute stroke therapy, Stroke, 28, 866, 10.1161/01.STR.28.4.866 Heiss, 1992, Progressive derangement of periinfarct viable tissue in ischemic stroke, J. Cereb. Blood Flow. Metab., 12, 193, 10.1038/jcbfm.1992.29 Bouts, 2013, Early identification of potentially salvageable tissue with MRI-based predictive algorithms after experimental ischemic stroke, J. Cereb. Blood Flow. Metab., 33, 1075, 10.1038/jcbfm.2013.51 Kaplan, 1991, Temporal thresholds for neocortical infarction in rats subjected to reversible focal cerebral ischemia, Stroke, 22, 1032, 10.1161/01.STR.22.8.1032 Astrup, 1981, Thresholds in cerebral ischemia-the ischemic penumbra, Stroke, 12, 723, 10.1161/01.STR.12.6.723 Fisher, 1999, Applications of diffusion-perfusion magnetic resonance imaging in acute ischemic stroke, Neurology, 52, 1750, 10.1212/WNL.52.9.1750 Campbell, 2019, Extending thrombolysis to 4 ⋅ 5–9h and wake-up stroke using perfusion imaging: a systematic review and meta-analysis of individual patient data, Lancet, 394, 139, 10.1016/S0140-6736(19)31053-0 Albers, 2018, Thrombectomy for stroke at 6 to 16 h with selection by perfusion imaging, N. Engl. J. Med., 378, 708, 10.1056/NEJMoa1713973 Ma, 2019, Thrombolysis guided by perfusion imaging up to 9 h after onset of stroke, N. Engl. J. Med., 380, 1795, 10.1056/NEJMoa1813046 Bruno, 2013, Simplified modified Rankin Scale questionnaire correlates with stroke severity, Clin. Rehabil., 27, 724, 10.1177/0269215512470674 Tong, 1998, Correlation of perfusion-and diffusion-weighted MRI with NIHSS score in acute ( < 6.5 h) ischemic stroke, Neurology, 50, 864, 10.1212/WNL.50.4.864 Boxerman, 2012, Clinical stroke penumbra: use of national institutes of health stroke scale as a surrogate for ct perfusion in patient triage for intra-arterial middle cerebral artery stroke therapy, Am. J. Neuroradiol., 33, 1893, 10.3174/ajnr.A3102 Prosser, 2005, Clinical-diffusion mismatch predicts the putative penumbra with high specificity, Stroke, 36, 1700, 10.1161/01.STR.0000173407.40773.17 Higashida, 2003, Trial design and reporting standards for intra-arterial cerebral thrombolysis for acute ischemic stroke, Stroke, 34, e109, 10.1161/01.STR.0000082721.62796.09 Fugate, 2013, What is meant by tici?, Am. J. Neuroradiol., 34, 1792, 10.3174/ajnr.A3496 Straka, 2010, Real-time diffusion-perfusion mismatch analysis in acute stroke, J. Magn. Reson. Imaging, 32, 1024, 10.1002/jmri.22338 Grady, 2006, Random walks for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 11, 1768, 10.1109/TPAMI.2006.233 Melouah, 2018, Overview of automatic seed selection methods for biomedical images segmentation., Int. Arab J. Inf. Technol., 15, 499 Maier, 2008, Automatic liver segmentation using the random walker algorithm, 56 Chen, 2015, SPARSE: Seed Point Auto-Generation for Random Walks Segmentation Enhancement in medical inhomogeneous targets delineation of morphological MR and CT images, J. Appl. Clin. Med. Phys., 16, 387, 10.1120/jacmp.v16i2.5324 Al-Kofahi, 2009, Improved automatic detection and segmentation of cell nuclei in histopathology images, IEEE Trans. Biomed. Eng., 57, 841, 10.1109/TBME.2009.2035102 He, 2014, An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm, PloS One, 9, 10.1371/journal.pone.0104437 Dong, 2016, Simultaneous segmentation of multiple organs using random walks, J. Inf. Process., 24, 320 Wighton, P., Sadeghi, M., Lee, T.K., Atkins, M.S., 2009, A fully automatic random walker segmentation for skin lesions in a supervised setting, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 1108–1115. Kabir, Y., Dojat, M., Scherrer, B., Forbes, F., Garbay, C., 2007, Multimodal MRI segmentation of ischemic stroke lesions, in: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 1595–1598. Mitra, 2014, Lesion segmentation from multimodal MRI using random forest following ischemic stroke, NeuroImage, 98, 324, 10.1016/j.neuroimage.2014.04.056 Maier, 2015, Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences, J. Neurosci. Methods, 240, 89, 10.1016/j.jneumeth.2014.11.011 McKinley, 2015, Segmenting the ischemic penumbra: a decision forest approach with automatic threshold finding, 275 McKinley, 2017, Fully automated stroke tissue estimation using random forest classifiers (FASTER), J. Cereb. Blood Flow Metab., 37, 2728, 10.1177/0271678X16674221 Gautam, 2019, Segmentation of ischemic stroke lesion from 3D MR images using random forest, Multimed. Tools Appl., 78, 6559, 10.1007/s11042-018-6418-2 Babu, 2019, An effective approach for sub-acute ischemic stroke lesion segmentation by adopting meta-heuristics feature selection technique along with hybrid naive bayes and sample-weighted random forest classification, Sens. Imaging, 20, 7, 10.1007/s11220-019-0230-6 Ghosh, 2012, Automated core-penumbra quantification in neonatal ischemic brain injury, J. Cereb. Blood Flow. Metab., 32, 2161, 10.1038/jcbfm.2012.121 Vupputuri, A., Ashwal, S., Tsao, B., Haddad, E., Ghosh, N., 2017, MRI based objective ischemic core-penumbra quantification in adult clinical stroke, in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 3012–3015. Feng, 2015, Segmentation of stroke lesions in multi-spectral MR images using bias correction embedded FCM and three phase level set, MICCAI Ischemic Stroke Lesion Segm., 3 Chen, 2017, Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks, NeuroImage: Clin., 15, 633, 10.1016/j.nicl.2017.06.016 Zhang, 2018, Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets, IEEE Trans. Med. Imaging, 37, 2149, 10.1109/TMI.2018.2821244 Kim, 2019, Evaluation of diffusion lesion volume measurements in acute ischemic stroke using encoder-decoder convolutional network, Stroke, 50, 1444, 10.1161/STROKEAHA.118.024261 Pinto, 2018, Stroke lesion outcome prediction based on MRI imaging combined with clinical information, Front. Neurol., 9, 1060, 10.3389/fneur.2018.01060 Inoue, H., 2018, Data augmentation by pairing samples for images classification, arXiv preprint arXiv:1801.02929. Ravuri, S., Vinyals, O., 2021, Seeing is not necessarily believing: Limitations of biggans for data augmentation, 〈https://openreview.net/forum?id=rJMw747l_4〉 (last accessed on 17 May 2021). Akkus, 2017, Deep learning for brain mri segmentation: state of the art and future directions, J. Digit. Imaging, 30, 449, 10.1007/s10278-017-9983-4 Maier, 2017, ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI, Med. Image Anal., 35, 250, 10.1016/j.media.2016.07.009 Kamnitsas, 2017, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Med. Image Anal., 36, 61, 10.1016/j.media.2016.10.004 Karthik, 2019, A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network, Appl. Soft Comput., 84, 10.1016/j.asoc.2019.105685 Subbanna, 2019, Stroke lesion segmentation in FLAIR MRI datasets using customized Markov random fields, Front. Neurol., 10, 541, 10.3389/fneur.2019.00541 Vupputuri, A., Dighade, S., Prasanth, P., Ghosh, N., 2018, Symmetry determined superpixels for efficient lesion segmentation of ischemic stroke from MRI, in: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 742–745. Rajinikanth, 2018, Segmentation of ischemic stroke lesion in brain MRI based on social group optimization and Fuzzy-Tsallis entropy, Arab. J. Sci. Eng., 43, 4365, 10.1007/s13369-017-3053-6 Vupputuri, 2019, Ischemic stroke segmentation in multi-sequence MRI by symmetry determined superpixel based hierarchical clustering, Comput. Biol. Med. Lee, 2019, Fully automated and real-time volumetric measurement of infarct core and penumbra in diffusion-and perfusion-weighted MRI of patients with hyper-acute stroke, J. Digit. Imaging, 1 Brunser, 2018, Diffusion-weighted imaging determinants for acute ischemic stroke diagnosis in the emergency room, Neuroradiology, 60, 687, 10.1007/s00234-018-2029-x Masoudi, 2021, Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research, J. Med. Imaging, 8, 10.1117/1.JMI.8.1.010901 Nyul, L.G., Udupa, J.K., 2000, Standardizing the mr image intensity scales: making mr intensities have tissue-specific meaning, in: Medical Imaging 2000: Image Display and Visualization, vol. 3976, International Society for Optics and Photonics, 496–504. Otsu, 1979, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man, Cybern., 9, 62, 10.1109/TSMC.1979.4310076 Cohen, 1960, A coefficient of agreement for nominal scales, Educ. Psychol. Meas., 20, 37, 10.1177/001316446002000104 Ghosh, 2011, Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury, J. Magn. Reson. Imaging, 33, 772, 10.1002/jmri.22488 Finch, 2003 Hong, 2020, Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs, Comput. Methods Prog. Biomed., 183, 10.1016/j.cmpb.2019.105065 Okorie, 2015, Role of diffusion-weighted imaging in acute stroke management using low-field magnetic resonance imaging in resource-limited settings, West Afr. J. Radiol., 22, 61, 10.4103/1115-3474.162168 Baird, 1998, Magnetic resonance imaging of acute stroke, J. Cereb. Blood Flow. Metab., 18, 583, 10.1097/00004647-199806000-00001 Zaro-Weber, 2019, Penumbra detection in acute stroke with perfusion magnetic resonance imaging: validation with 15o-positron emission tomography, Ann. Neurol., 85, 875, 10.1002/ana.25479 Wouters, 2017, A comparison of relative time to peak and tmax for mismatch-based patient selection, Front. Neurol., 8, 539, 10.3389/fneur.2017.00539 Essig, 2013, Perfusion mri: the five most frequently asked technical questions, Am. J. Roentgenol., 200, 24, 10.2214/AJR.12.9543