A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance

Joo, Bio; Ahn, Sung Soo; Yoon, Pyeong Ho; Bae, Soong June; Sohn, Beomseok; Lee, Yong Eun; Bae, Jun Ho; Park, Moo Sung; Choi, Hyun Seok; Lee, Seung Koo

doi:10.1007/s00330-020-06966-8

Cited by 55 publications

(37 citation statements)

References 24 publications

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“…The training set for this software was developed on the basis of the neuroradiologist's interpretation of TOF-MRA. 15 Furthermore, the previous results of deep learning-based CAD were comparable with not better than those of the radiologist.…”

Section: Discussionmentioning

confidence: 72%

“…The details of the algorithm have been published elsewhere. 15 To develop the original study model, we randomly extracted 600 patients from our hospital from 2014 to 2016. For validation, 110 patients from another institution were prepared for the external test set.…”

Section: Cad Softwarementioning

confidence: 99%

“…[10][11][12][13][14] A previous study used a deep learning-based CAD software for the automated detection and localization of intracranial aneurysms on MRA and validated its high sensitivity and specificity via internal and external test sets. 15 Due to the previously mentioned relative shortage of radiologists and increasing workload, in practice, clinicians sometimes have to meet the patient and review the MRA without an official report from a neuroradiologist. Park et al 11 investigated the performance of clinicians in identifying intracranial aneurysms using CT angiography, assisted by a deep learning model.…”

mentioning

confidence: 99%

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Deep Learning–Based Software Improves Clinicians’ Detection Sensitivity of Aneurysms on Brain TOF-MRA

Sohn¹,

Park

Choi

et al. 2021

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:The detection of cerebral aneurysms on MRA is a challenging task. Recent studies have used deep learning-based software for automated detection of aneurysms on MRA and have reported high performance. The purpose of this study was to evaluate the incremental value of using deep learning-based software for the detection of aneurysms on MRA by 2 radiologists, a neurosurgeon, and a neurologist.MATERIALS AND METHODS: TOF-MRA examinations of intracranial aneurysms were retrospectively extracted. Four physicians interpreted the MRA blindly. After a washout period, they interpreted MRA again using the software. Sensitivity and specificity per patient, sensitivity per lesion, and the number of false-positives per case were measured. Diagnostic performances, including subgroup analysis of lesions, were compared. Logistic regression with a generalized estimating equation was used.RESULTS: A total of 332 patients were evaluated; 135 patients had positive findings with 169 lesions. With software assistance, patient-based sensitivity was statistically improved after the washout period (73.5% versus 86.5%, P , .001). The neurosurgeon and neurologist showed a significant increase in patient-based sensitivity with software assistance (74.8% versus 85.2%, P ¼ .03, and 56.3% versus 84.4%, P , .001, respectively), while the number of false-positive cases did not increase significantly (23 versus 30, P ¼ .20, and 22 versus 24, P ¼ .75, respectively).CONCLUSIONS: Software-aided reading showed significant incremental value in the sensitivity of clinicians in the detection of aneurysms on MRA without a significant increase in false-positive findings, especially for the neurosurgeon and neurologist. Software-aided reading showed equivocal value for the radiologist.

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Section: Discussionmentioning

confidence: 72%

Section: Cad Softwarementioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning–Based Software Improves Clinicians’ Detection Sensitivity of Aneurysms on Brain TOF-MRA

Sohn¹,

Park

Choi

et al. 2021

AJNR Am J Neuroradiol

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“…ere are many research studies about intracranial aneurysm detection and classification on the basis of the 2D or 3D CNN [18,19,21,24]. To the best of our knowledge, however, this is the first time that 1D CNN based is used in this application.…”

Section: Discussionmentioning

confidence: 99%

“…Allison used Computer Tomograph Angiography (CTA) images of the brain to construct a HeadXNet model to segment aneurysms [18], which predicted aneurysms with high sensitivity. Bio Joo conducted IAs detection based on the 3D ResNet leading to better result [19]. A cascade strategy was proposed to automatically detect Cerebral MicroBleeds (CMBs) from MR images using 3D CNN [20].…”

Section: Introductionmentioning

confidence: 99%

1D CNN-Based Intracranial Aneurysms Detection in 3D TOF-MRA

Hou¹,

Mei²,

Gui³

et al. 2020

Complexity

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How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography (3D TOF MRA) images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- (MIP-) based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network (CNN) is established to do classification. It has been shown that the MIP-based method can reduce the demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests (ROIs) along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close.

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