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Quantitative analysis of brain herniation from non-contrast CT images using deep learning by Manas Kumar Nag; Akshat Gupta; A.S. Hariharasudhan; Anup Kumar Sadhu; Abir Das; Nirmalya Ghosh is a Neuroscience article available to read on EtoBox.

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## Background: Brain herniation is one of the fatal outcomes of increased intracranial pressure (icp). it is caused due to the presence of hematoma or tumor mass in the brain. ideal midline (iml) divides the healthy brain into two (right and left) nearly equal hemispheres. in the presence of hematoma, the midline tends to shift from its original position to the contralateral side of the mass and thus develops a deformed midline (dml). ## New method: In this study, a convolutional neural network (cnn) was used to predict the deformed left and right hemispheres. the proposed algorithm was validated with non-contrast computed tomography (ncct) of (n = 45) subjects with two types of brain hemorrhages - epidural hemorrhage (edh): (n = 5) and intra-parenchymal hemorrhage (iph): (n = 40)). ## Results: The method demonstrated excellent potential in automatically predicting mls with the average errors of 1.29 mm by location, 66.4 mm by 2d area, and 253.73 mm by 3d volume. estimated mls could be well correlated with other clinical markers including hematoma volume - r = 0.86 (edh); 0.48 (iph) and a radiologist-defined severity score (rss) - r = 0.62 (edh); 0.57 (iph). rss was found to be eve

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Author

Manas Kumar Nag; Akshat Gupta; A.S. Hariharasudhan; Anup Kumar Sadhu; Abir Das; Nirmalya Ghosh

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0165-0270)

Published

2020

Language

EN

Field

Neuroscience (Life Sciences)