CC BY 4.0 · Indian Journal of Neurotrauma 2024; 21(01): 063-066
DOI: 10.1055/s-0042-1760417
Brief Report

Automated Detection of Lesions in Patients with Traumatic Brain Injury using Brain CT Images: Concept Note and Proposed Method

Amit Agrawal
1   Department of Neurosurgery, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
,
Rakesh Mishra
2   Department of Neurosurgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
› Author Affiliations

Abstract

Accurate and early interpretation of CT scan images in TBI patients reduces the critical time for diagnosis and management. As mentioned in other studies, automated CT interpretation using the feature extraction method is a rapid and accurate tool. Despite several studies on the machine and deep learning employing algorithms for automated CT interpretations, it has its challenges. This study presents a concept note and proposes a feature-based computer-aided diagnostic method to perform automated CT interpretation in TBI. The method consists of preprocessing, segmentation, and extraction. We have described a simple way of classifying the CT scan head into five circumferential zones in this method. The zones are identified quickly based on the anatomic characteristics and specific pathologies that affect each zone. Then, we have provided an overview of different pathologies affecting each of these zones. Utilizing these zones for automated CT interpretation will also be a helpful resource for concerned physicians during the odd and rush hours.



Publication History

Article published online:
18 January 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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  • References

  • 1 Dewan MC, Rattani A, Gupta S. et al. Estimating the global incidence of traumatic brain injury. J Neurosurg 2018; 1-18
  • 2 Le TH, Gean AD. Imaging of head trauma. Semin Roentgenol 2006; 41 (03) 177-189
  • 3 Al-Nakshabandi NA. The swirl sign. Radiology 2001; 218 (02) 433-433
  • 4 Greenberg J, Cohen WA, Cooper PR. The “hyperacute” extraaxial intracranial hematoma: computed tomographic findings and clinical significance. Neurosurgery 1985; 17 (01) 48-56
  • 5 Chawla M, Sharma S, Sivaswamy J, Kishore L. A method for automatic detection and classification of stroke from brain CT images. Engineering in Medicine and Biology Society, 2009 EMBC 2009 Annual International Conference of the IEEE; 2009: IEEE: 3581-3584
  • 6 Ripollés P, Marco-Pallarés J, de Diego-Balaguer R. et al. Analysis of automated methods for spatial normalization of lesioned brains. Neuroimage 2012; 60 (02) 1296-1306
  • 7 Węgliński T, Fabijańska A. Image segmentation algorithms for diagnosis support of hydrocephalus in children. Automatyka/Akademia Górniczo-Hutnicza im Stanisława Staszica w Krakowie 2011; 15 (03) 309-319
  • 8 Al-Ashwal RH, Supriyanto E, Rani NAB. et al. Digital processing for computed tomography images: brain tumor extraction and histogram analysis. Math Comput Contemp Sci 14th International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (MMACTEE13); 2012: 119-128
  • 9 Bertè F, Lamponi G, Bramanti P, Calabrò RS. Automatic brain matter segmentation of computed tomography images using a statistical model: a tool to gain working time!. Neuroradiol J 2015; 28 (05) 460-467
  • 10 Cootes TF, Edwards GJ, Taylor CJ. Active appearance models. IEEE Trans Pattern Anal Mach Intell 2001; 26 (06) 681-685
  • 11 Khawaja MA, Aziz MZ, Iqbal N. Effectual lung segmentation for CAD systems using CT scan images. Multitopic Conference, 2004 Proceedings of INMIC 2004 8th International; 2004: IEEE: 49-54
  • 12 Muschelli J, Ullman NL, Mould WA, Vespa P, Hanley DF, Crainiceanu CM. Validated automatic brain extraction of head CT images. Neuroimage 2015; 114: 379-385