Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: A fuzzy approach

Davis, A.; Gordillo, Nelly; Montseny, E.; Aymerich, Francesc Xavier; López-Córdova, M.; Mej­ía, José; Ortega, Loréna; Mederos, Boris

doi:10.1016/j.bspc.2018.05.037

Cited by 11 publications

(5 citation statements)

References 39 publications

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“…Previously, a deep-learning model was used by Nishio et al to detect AIS lesions [22]. In another study, Davis developed a method based on the symmetry of cerebral hemispheres for detecting the early stages of ischemic stroke [23]. However, the robustness of the methods was not verified.…”

Section: Discussionmentioning

confidence: 99%

Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model

Zhou¹,

Lv²,

Zhu³

et al. 2022

Theranostics

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Rationale: Although non-contrast computed tomography (NCCT) is the recommended examination for the suspected acute ischemic stroke (AIS), it cannot detect significant changes in the early infarction. We aimed to develop a deep-learning model to identify early invisible AIS in NCCT and evaluate its diagnostic performance and capacity for assisting radiologists in decision making. Methods: In this multi-center, multi-manufacturer retrospective study, 1136 patients with suspected AIS but invisible lesions in NCCT were collected from two geographically distant institutions between May 2012 to May 2021. The AIS lesions were confirmed based on the follow-up diffusion-weighted imaging and clinical diagnosis. The deep-learning model was comprised of two deep convolutional neural networks to locate and classify. The performance of the model and radiologists was evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and accuracy values with 95% confidence intervals. Delong's test was used to compare the AUC values, and a chi-squared test was used to evaluate the rate differences. Results: 986 patients (728 AIS, median age, 55 years, interquartile range [IQR]: 47-65 years; 664 males) were assigned to the training and internal validation cohorts. 150 patients (74 AIS, median age, 63 years, IQR: 53-75 years; 100 males) were included as an external validation cohort. The AUCs of the model were 83.61% (sensitivity, 68.99%; specificity, 98.22%; and accuracy, 89.87%) and 76.32% (sensitivity, 62.99%; specificity, 89.65%; and accuracy, 88.61%) for the internal and external validation cohorts based on the slices. The AUC of the model was much higher than that of two experienced radiologists (65.52% and 59.48% in the internal validation cohort; 64.01% and 64.39% in external validation cohort; all P < 0.001). The accuracy of two radiologists increased from 62.00% and 58.67% to 92.00% and 84.67% when assisted by the model for patients in the external validation cohort. Conclusions: This deep-learning model represents a breakthrough in solving the challenge that early invisible AIS lesions cannot be detected by NCCT. The model we developed in this study can screen early AIS and save more time. The radiologists assisted with the model can provide more effective guidance in making patients' treatment plan in clinic.

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Section: Discussionmentioning

confidence: 99%

Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model

Zhou¹,

Lv²,

Zhu³

et al. 2022

Theranostics

View full text Add to dashboard Cite

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“…On the other hand, other work related to ncCT was conducted on the basis of some empirical assumption. Davis et al 10 and Yahiaoui et al 23 assumed that stroke would only occur on unilateral brain. Hence, Davis et al 10 divided the whole brain into several contralateral subregions while Yahiaoui et al 23 extracted patches in a contralateral way, which is not able to tackle the problem of contralateral stroke.…”

Section: Discussionmentioning

confidence: 99%

“…Davis et al 10 and Yahiaoui et al 23 assumed that stroke would only occur on unilateral brain. Hence, Davis et al 10 divided the whole brain into several contralateral subregions while Yahiaoui et al 23 extracted patches in a contralateral way, which is not able to tackle the problem of contralateral stroke. In practice, limitation of treatment time for ischemic stroke and higher applicability of ncCT imply the great clinical significance of our method, which avoids vague assumption as well.…”

Section: Discussionmentioning

confidence: 99%

“…A. Przelaskowski et al 9 utilized a wavelet‐based image processing method to achieve data denoising and local contrast enhancement on nonenhanced computed tomography but their work only perceived occurrence of stroke instead of its location. Achieving similar function, Davis et al 10 developed an automated method to identify changes in parenchymal density in ncCT for detecting early stages of ischemic stroke. Nonpathological tissues like cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM) in Hounsfield Units were characterized using fuzzy logic technique, which added more clinical information to the stroke diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Identification of invisible ischemic stroke in noncontrast CT based on novel two‐stage convolutional neural network model

Chen

Lin

et al. 2021

Medical Physics

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Purpose Early identification of ischemic stroke lesion regions plays a vital role in its treatments like thrombolytic therapy and patients’ recovery. Noncontrast computed tomography (ncCT) is the most widespread imaging modality in emergency departments. Unfortunately, it is extremely hard to distinguish the lesion from healthy tissue during the hyper‐acute phase of stroke. In this paper, a two‐stage convolutional neural network‐based method was proposed to identify the invisible ischemic stroke from ncCT. Methods In order to combine the global and local information of images effectively, a cascaded structure with two coordinated networks was used to detect the suspicious stroke regions on the whole and optimize the detailed localization. In the first stage, an end‐to‐end U‐net with adaptive threshold was proposed to integrate global position, symmetry and gray texture information to detect the suspicious regions. After reducing the interference from most normal regions, a ResNet‐based patch classification network was used to eliminate some false positive samples on suspicious regions by mining deeper image features, contributing to a more precise localization of stroke. Finally, a MAP model was used to optimize the result by combining the classification results of each patch with their spatial constraint information. Results Three independent experiments, that is, training and testing on dataset from one hospital, on the combination of two, and on the two respectively, were performed on a total of 277 cases from two hospitals to validate the proposed model, The proposed method achieved identification accuracy of 91.89%, 87.21%, and 85.71% in the three experiments, and the final localization accuracy in terms of precise localization of stroke were 82.35%, 83.02%, and 81.40%, respectively, which indicated the robustness and clinical values of the method. Conclusions There are some deep image feature differences between stroke region and normal region on ncCT images. The proposed two‐stage convolutional neural network model can well seize these features and use them to effectively identify and locate stroke.

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“…Many studies on stroke image analysis have attempted to improve lesion visibility on CT, such as lesion-totissue contrast amplification (22,23), voxelwise comparison (16,17,24,25), and texture analysis (26)(27)(28)(29)(30). Despite heterogeneous methodologies and sensitivities (31), substantial evidence indicates that certain radiological features, even if they are subtle or invisible, could be captured to improve human perception of the underlying pathologies.…”

mentioning

confidence: 99%

Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks

Hu¹,

Zhang²,

Wu³

et al. 2022

Ann Transl Med

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Background: Difficulties in detecting brain lesions in acute ischemic stroke (AIS) have convinced researchers to use computed tomography (CT) to scan for and magnetic resonance imaging (MRI) to search for these lesions.This work aimed to develop a generative adversarial network (GAN) model for CT-to-MR image synthesis and evaluate reader performance with synthetic MRI (syn-MRI) in detecting brain lesions in suspected patients.Methods: Patients with primarily suspected AIS were randomly assigned to the training (n=140) or testing (n=53) set. Emergency CT and follow-up MR images in the training set were used to develop a GAN model to generate syn-MR images from the CT data in the testing set. The standard reference was the manual segmentations of follow-up MR images. Image similarity was evaluated between syn-MRI and the ground truth using a 4-grade visual rating scale, the peak signal-to-noise ratio (PSNR), and the structural similarity index measure (SSIM). Reader performance with syn-MRI and CT was evaluated and compared on a perpatient (patient detection) and per-lesion (lesion detection) basis. Paired t-tests or Wilcoxon signed-rank tests were used to compare reader performance in lesion detection between the syn-MRI and CT data.Results: Grade 2-4 brain lesions were observed on syn-MRI in 92.5% (49/53) of the patients, while the remaining syn-MRI data showed no lesions compared to the ground truth. The GAN model exhibited a weak PSNR of 24.30 dB but a favorable SSIM of 0.857. Compared with CT, syn-MRI led to an increase in the overall sensitivity from 38% (57/150) to 82% (123/150) in patient detection and from 4% (68/1,620) to 16% (262/1,620) in lesion detection (R=0.32, corrected P<0.001), but the specificity in patient detection decreased from 67% (6/9) to 33% (3/9). An additional 75% (70/93) of patients and 15% (77/517) of lesions missed on CT were detected on syn-MRI. Conclusions:The GAN model holds potential for generating synthetic MR images from noncontrast CT data and thus could help sensitively detect individuals among patients with suspected AIS. However, the image similarity performance of the model needs to be improved, and further expert discrimination is strongly recommended.

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Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: A fuzzy approach

Cited by 11 publications

References 39 publications

Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model

Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model

Identification of invisible ischemic stroke in noncontrast CT based on novel two‐stage convolutional neural network model

Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks

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