Automatic Midline Shift Detection in Traumatic Brain Injury

Hooshmand, Mohsen; Soroushmehr, S. M. Reza; Williamson, Craig A.; Gryak, Jonathan; Najarian, Kayvan

doi:10.1109/embc.2018.8512243

Cited by 6 publications

(14 citation statements)

References 13 publications

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“…Another approach for detecting hematoma in CT imagery involves the segmentation of hematoma regions, and a set of usual and handcrafted features are extracted to improve the classification performance [ 28 , 34 , 37 , 72 ]. Shahangian et al [ 42 ] applied a modified DRLSE to segment hematoma regions, and used handcrafted shape and texture features to classify the hematoma into four subtypes.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

“…All of the optimal candidate points are thus connected to form the dML, and the MLS is quantified using two measurements, namely, the area ratio and the maximum shift distance. Hosshmand et al [ 28 ] computed the dML based on the geometrical patterns of ventricles to estimate the MLS.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

“…The initial interpretation by inexperienced readers is often tedious, and results in misinterpretation and clinical consequences [ 24 , 25 ]. Moreover, manual segmentation of hematoma or midline structures from selected CT slices is challenging due to reasons such as variation in pixel-wise intensity, uneven boundaries, high contrast of tissues, and the presence of noise and artefact [ 26 , 27 , 28 ]. Furthermore, the set of features required for CT-based ICP estimation cannot be readily identified by visual inspection, and is also subject to intra-observer and inter-observer variability [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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“…5,7 MLS represents the extent of brain displacement resulting from the injury itself. 3,7 For instance, analysis of data from the National Traumatic Coma Data Bank revealed that among 18 patients who experienced initial speech ability but later deteriorated, 7 had an MLS exceeding 15 mm. 3 The measurement of MLS serves as a valuable tool in assessing the severity of TBI and predicting the related clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…When midline structures undergo a shift, it is presumed to represent a mass lesion on the side from which the midline is displaced, as symmetry plays a critical role in radiologic brain evaluation. 7 MLS is defined as the displacement of the brain from its assumed center line, and it can be quantified by measuring the perpendicular distance between the IML and the shifted SP, and a common practice is to measure the maximum distance from the midline formed by the anterior falx and posterior falx to the SP at the axial level of the foramen of Monro. 8,9,11 The Brain Trauma Foundation initially proposed a manual measurement method for MLS.…”

Section: Introductionmentioning

confidence: 99%

Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

Agrawal,

Joshi,

Poonamallee

2024

Indian Journal of Neurotrauma

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Traumatic brain injury (TBI) often results in midline shift (MLS) that is a critical indicator of the severity and prognosis of head injuries. Automated analysis of MLS from head computed tomography (CT) scans using artificial intelligence (AI) techniques has gained much attention in the past decade and has shown promise in improving diagnostic efficiency and accuracy. This review aims to summarize the current state of research on AI-based approaches for MLS analysis in TBI cases, identify the methodologies employed, evaluate the performance of the algorithms, and draw conclusions regarding their potential clinical applicability. A comprehensive literature search was conducted, identifying 15 distinctive publications. The identified articles were analyzed for their focus on MLS detection and quantification using AI techniques, including their choice of AI algorithms, dataset characteristics, and methodology. The reviewed articles covered various aspects related to MLS detection and quantification, employing deep neural networks trained on two-dimensional or three-dimensional CT imaging datasets. The dataset sizes ranged from 11 patients' CT scans to 25,000 CT images. The performance of the AI algorithms exhibited variations in accuracy, sensitivity, and specificity, with sensitivity ranging from 70 to 100%, and specificity ranging from 73 to 97.4%. AI-based approaches utilizing deep neural networks have demonstrated potential in the automated detection and quantification of MLS in TBI cases. However, different researchers have used different techniques; hence, critical comparison is difficult. Further research and standardization of evaluation protocols are needed to establish the reliability and generalizability of these AI algorithms for MLS detection and quantification in clinical practice. The findings highlight the importance of AI techniques in improving MLS diagnosis and guiding clinical decision-making in TBI management.

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Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

Létourneau-Guillon

Camirand

Guilbert

et al. 2020

Neuroimaging Clinics of North America

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Automatic Midline Shift Detection in Traumatic Brain Injury

Cited by 6 publications

References 13 publications

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics

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