Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans

Nguyen, Nguyen P.; Yoo, Youngjin; Chekkoury, Andrei; Eibenberger, Eva; Re, Thomas J.; Das, Jyotipriya; Balachandran, Abishek; Lui, Yvonne W.; Sanelli, Pina C.; Schroeppel, Thomas J.; Bodanapally, Uttam K.; Nicolaou, Savvas; White, Tommi; Bunyak, Filiz; Comanicìu, Dorin; Gibson, Eli

doi:10.1109/iccvw54120.2021.00059

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…[ 3 ] Another method involves measuring the maximum distance from the midline formed by the anterior falx and posterior falx to the septum pellucidum at the axial level of the foramen of Monro. [ 3 , 6 , 8 ] Another approach suggested measuring the distance from a line connecting the most anterior and posterior visible points on the falx to the farthest point on the septum pellucidum. [ 9 ]…”

Section: Discussionmentioning

confidence: 99%

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Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans

Agrawal,

Joshi,

Bahel

et al. 2024

JNRP

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Objectives: Midline shift (MLS) is a critical indicator of the severity of brain trauma and is even suggestive of changes in intracranial pressure. At present, radiologists have to manually measure the MLS using laborious techniques. Automatic detection of MLS using artificial intelligence can be a cutting-edge solution for emergency health-care personnel to help in prompt diagnosis and treatment. In this study, we sought to determine the accuracy and the prognostic value of our screening tool that automatically detects MLS on computed tomography (CT) images in patients with traumatic brain injuries (TBIs). Materials and Methods: The study enrolled TBI cases, who presented at the Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi. Institutional ethics committee permission was taken before starting the study. The data collection was carried out for over nine months, i.e., from January 2020 to September 2020. The data collection included head CT scans, patient demographics, clinical details as well as radiologist’s reports. The radiologist’s reports were considered the “gold standard” for evaluating the MLS. A deep learning-based three dimensional (3D) convolutional neural network (CNN) model was developed using 176 head CT scans. Results: The developed 3D CNN model was trained using 156 scans and was tested on 20 head CTs to determine the accuracy and sensitivity of the model. The screening tool was correctly able to detect 7/10 MLS cases and 4/10 non-MLS cases. The model showed an accuracy of 55% with high specificity (70%) and moderate sensitivity of 40%. Conclusion: An automated solution for screening the MLS can prove useful for neurosurgeons. The results are strong evidence that 3D CNN can assist clinicians in screening MLS cases in an emergency setting.

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Section: Discussionmentioning

confidence: 99%

“…On the other hand, Nguyen et al . [ 8 ] proposed a landmark-based system with two modules for MLS detection and quantification using CQ500 dataset achieving a total accuracy of 0.89. Most research efforts focus on MLS quantification, some primarily emphasize measuring the shift rather than its detection.…”

Section: Discussionmentioning

confidence: 99%

Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans

Agrawal,

Joshi,

Bahel

et al. 2024

JNRP

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“…When comparing the performance of Nguyens et al's method to Chilamkurthy et al's work, it was observed that Nguyens et al achieved higher detection performance in terms of AUC for the equivalent-size dataset of 25k images, but lower AUC was obtained with the smaller CQ500 dataset. 11,19 In 2017, Wang et al developed a method for automatically measuring MLS with an average processing time of around 10 seconds. This method involved plotting a weighted midline (WML) based on pixel intensities, with higher weights assigned to darker portions.…”

Section: The Symmetry-based Approachmentioning

confidence: 99%

“…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. 12 It involved measuring the intracranial width at the level of the foramen Monro and then determining the distance from the inner skull to the SP.…”

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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“…Liu et al [15] utilized six anatomical markers to define and gauge the distance of midline shift. Nguyen et al [16] implemented a U-Net [17] based cascade network to locate landmarks in 2D and 3D for quantizing the midline shift. Yan et al [18] used a modified key-point network to identify the key points of septum pellucidum and measure the midline shift while analyzing the clinical parameters connected with the results.…”

Section: Introductionmentioning

confidence: 99%

Dual-path U-transformer network for assessment of brain midline shift in hemorrhagic stroke

Zhang,

Liang,

Guo

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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Midline shift is an important clinical indicator of the severity of hemorrhagic stroke and holds significance in a physician's clinical diagnosis. Segmentation-based methods for midline shift assessment are prevalent in the field, but the full utilization of global information within network remains a challenge. In addition, empirical integration with clinical knowledge is also essential. In this study, we developed a two-stage method for automatic midline shift assessment. Firstly, in the midline identification step, we proposed a Dual-Path U-Transformer to segment the brain midline. The Dual-Path U-Transformer can better capitalize on global information by integrating self-attention mechanism, while still retaining the characteristics of U-Net in making full use of local information and combining high and low dimensional features. In the second stage, according to clinical knowledge learned from clinical expert, we calculated the maximum shift distance for assessment of brain midline shift, determining whether each case has surgical indication. In the experiments process, we use 5-fold cross validation to train and validate the proposed model. Compared with traditional U-Net based method and transformer-based method, the proposed Dual-Path U-Transformer based method performed the best HD and Dice performance on our inhouse dataset. And the experiment results confirmed that the Dual-Path U-Transformer based exhibited excellent accuracy in the second stage of midline shift assessment.

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Brain midline shift detection and quantification by a cascaded deep network pipeline on non-contrast computed tomography scans

Cited by 5 publications

References 18 publications

Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans

Three dimensional convolutional neural network-based automated detection of midline shift in traumatic brain injury cases from head computed tomography scans

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

Dual-path U-transformer network for assessment of brain midline shift in hemorrhagic stroke

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