PURPOSE: To extend the ISODATA image segmentation method to characterize tissue damage in stroke, by generating an MRI score for each tissue that corresponds to its histological damage. MATERIALS AND METHODS: After preprocessing and segmentation (using ISODATA clustering), the proposed method scores tissue regions between 1 and 100. Score 1 is assigned to normal brain matter (white or gray matter), and score 100 to cerebrospinal fluid (CSF). Lesion zones are assigned a score based on their relative levels of similarities to normal brain matter and CSF. To evaluate the method, 15 rats were imaged by a 7T MRI system at one of three time points (acute, subacute, chronic) after MCA occlusion. Then they were killed and their brains were sliced and prepared for histological studies. MRI of two or three slices of each rat brain (using two DWI (b = 400, b = 800), one PDWI, one T2WI, and one T1WI) was performed, and an MRI score between 1 and 100 was determined for each region. Segmented regions were mapped onto the histology images and scored on a scale of 1-10 by an experienced pathologist. The MRI scores were validated by comparison with histology scores. To this end, correlation coefficients between the two scores (MRI and histology) were determined. RESULTS: Experimental results showed excellent correlations between MRI and histology scores at different time points. Depending on the reference tissue (gray matter or white matter) used in the standardization, the correlation coefficients ranged from 0.73 (P < 0.0001) to 0.78 (P < 0.0001) using the entire dataset, including acute, subacute, and chronic time points. This suggests that the proposed multiparametric approach accurately identified and characterized ischemic tissue in a rat model of cerebral ischemia at different stages of stroke evolution. CONCLUSION: The proposed approach scores tissue regions and characterizes them using unsupervised clustering and multiparametric image analysis techniques. The method can be used for a variety of applications in the field of computer-aided diagnosis and treatment, including evaluation of response to treatment. For example, volume changes for different zones of the lesion over time (e.g., tissue recovery) can be evaluated. Copyright 2003 Wiley-Liss, Inc.
PURPOSE: To extend the ISODATA image segmentation method to characterize tissue damage in stroke, by generating an MRI score for each tissue that corresponds to its histological damage. MATERIALS AND METHODS: After preprocessing and segmentation (using ISODATA clustering), the proposed method scores tissue regions between 1 and 100. Score 1 is assigned to normal brain matter (white or gray matter), and score 100 to cerebrospinal fluid (CSF). Lesion zones are assigned a score based on their relative levels of similarities to normal brain matter and CSF. To evaluate the method, 15 rats were imaged by a 7T MRI system at one of three time points (acute, subacute, chronic) after MCA occlusion. Then they were killed and their brains were sliced and prepared for histological studies. MRI of two or three slices of each rat brain (using two DWI (b = 400, b = 800), one PDWI, one T2WI, and one T1WI) was performed, and an MRI score between 1 and 100 was determined for each region. Segmented regions were mapped onto the histology images and scored on a scale of 1-10 by an experienced pathologist. The MRI scores were validated by comparison with histology scores. To this end, correlation coefficients between the two scores (MRI and histology) were determined. RESULTS: Experimental results showed excellent correlations between MRI and histology scores at different time points. Depending on the reference tissue (gray matter or white matter) used in the standardization, the correlation coefficients ranged from 0.73 (P < 0.0001) to 0.78 (P < 0.0001) using the entire dataset, including acute, subacute, and chronic time points. This suggests that the proposed multiparametric approach accurately identified and characterized ischemic tissue in a rat model of cerebral ischemia at different stages of stroke evolution. CONCLUSION: The proposed approach scores tissue regions and characterizes them using unsupervised clustering and multiparametric image analysis techniques. The method can be used for a variety of applications in the field of computer-aided diagnosis and treatment, including evaluation of response to treatment. For example, volume changes for different zones of the lesion over time (e.g., tissue recovery) can be evaluated. Copyright 2003 Wiley-Liss, Inc.
Authors: Inge A Mulder; Artem Khmelinskii; Oleh Dzyubachyk; Sebastiaan de Jong; Nathalie Rieff; Marieke J H Wermer; Mathias Hoehn; Boudewijn P F Lelieveldt; Arn M J M van den Maagdenberg Journal: Front Neuroinform Date: 2017-01-31 Impact factor: 4.081
Authors: Anthony T O'Brien; Rivadavio Amorim; R Jarrett Rushmore; Uri Eden; Linda Afifi; Laura Dipietro; Timothy Wagner; Antoni Valero-Cabré Journal: Front Hum Neurosci Date: 2016-11-09 Impact factor: 3.169
Authors: Stefan Pszczolkowski; Zhe K Law; Rebecca G Gallagher; Dewen Meng; David J Swienton; Paul S Morgan; Philip M Bath; Nikola Sprigg; Rob A Dineen Journal: Comput Biol Med Date: 2019-01-29 Impact factor: 4.589
Authors: Mark J R J Bouts; Susan V Westmoreland; Alex J de Crespigny; Yutong Liu; Mark Vangel; Rick M Dijkhuizen; Ona Wu; Helen E D'Arceuil Journal: BMC Neurosci Date: 2015-12-15 Impact factor: 3.288