Semi‐automated infarct segmentation from follow‐up noncontrast CT scans in patients with acute ischemic stroke

Kuang, Hulin; Menon, Bijoy K; Qiu, Wu

doi:10.1002/mp.13703

Cited by 16 publications

(14 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The median lesion volumes for the training and validation sets were 40.4 [14.1-96.3] cm 3 and 41.5 [20.0-107.1] cm 3 , respectively. As the out-of-distribution holdout test set was segmented by multiple observers, the manual segmentation lesion volume for each example was defined as the average volume calculated across observers.…”

Section: Resultsmentioning

confidence: 99%

“…The validation dataset was used to estimate the optimum minimum object size cut-off and the hole-filling kernel radius. The minimum object size cut-off was optimized first, by varying the cut-off range from 0.3 cm 3 to 2.5 cm 3 . The cut-off that maximized the DSC was 1.5 cm 3 and was used for post-processing.…”

Section: E Post-processing Of Cnn Segmentationsmentioning

confidence: 99%

“…Manual lesion segmentation in NCCT images is time consuming and associated with high inter-observer variability. Semi-automatic lesion segmentation tools have been developed [3], [4]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

Non-contrast computed tomography (NCCT) is commonly used for volumetric follow-up assessment of ischemic strokes. However, manual lesion segmentation is time-consuming and subject to high inter-observer variability. The aim of this study was to develop and establish a baseline convolutional neural network (CNN) model for automatic NCCT lesion segmentation. A total of 252 multi-center clinical NCCT datasets, acquired from 22 centers, and corresponding manual segmentations were used to train (204 datasets) and validate (48 datasets) a 3D multi-scale CNN model for lesion segmentation. Post-processing methods were implemented to improve the CNN-based lesion segmentations. The final CNN model and post-processing method was evaluated using 39 out-of-distribution holdout test datasets, acquired at seven centers that did not contribute to the training or validation datasets. Each test image was segmented by two or three neuroradiologists. The Dice similarity coefficient (DSC) and predicted lesion volumes were used to evaluate the segmentations. The CNN model achieved a mean DSC score of 0.47 on the validation NCCT datasets. Post-processing significantly improved the DSC to 0.50 (P < 0.01). On the holdout test set, the CNN model achieved a mean DSC score of 0.42, which was also significantly improved to 0.45 (P < 0.05) by post-processing. Importantly, the automatically segmented lesion volumes were not significantly different from the lesion volumes determined by the expert observers (P > 0.05) and showed excellent agreement with manual lesion segmentation volumes (intraclass correlation coefficient, ICC = 0.88). The proposed CNN model can automatically and reliably segment ischemic stroke lesions in clinical NCCT datasets. Post-processing techniques can further improve accuracy. As the model was trained and evaluated on datasets from multiple centers, it is broadly applicable and is publicly available. INDEX TERMS Artificial neural networks, brain, computed tomography, computer-assisted image analysis, convolutional neural networks, deep learning, machine learning, stroke.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: E Post-processing Of Cnn Segmentationsmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Three deep learning approaches have been proposed by the group of Kuang, Menon, and Qiu to segment follow-up NCCT scans to measure post-treatment cerebral infarct volumes for evaluating the effectiveness of endovascular therapy of acute ischemic stroke patients. Kuang, Menon & Qiu (2019a) presented an infarct segmentation method that combines machine learning exploiting cascaded RF and interactive segmentation. The method includes three major steps as reported by Kuang, Menon & Qiu (2019a): expert initialization, RF learning and classification (with a two-stage training and testing classifier), and convex optimization-based segmentation.…”

Section: Ai-based Methodsmentioning

confidence: 99%

“…Kuang, Menon & Qiu (2019a) presented an infarct segmentation method that combines machine learning exploiting cascaded RF and interactive segmentation. The method includes three major steps as reported by Kuang, Menon & Qiu (2019a): expert initialization, RF learning and classification (with a two-stage training and testing classifier), and convex optimization-based segmentation. The initialization step requires the user's input knowledge to pre-label some voxels in the infarcted region and background on a few axial slices aiming to lessen the detected false positives (making in this way the method semi-automated).…”

Section: Ai-based Methodsmentioning

confidence: 99%

Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods

Nowinski

Walecki

Półtorak-Szymczak

et al. 2020

PeerJ

View full text Add to dashboard Cite

Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.

show abstract

A CNN transfer learning‐based approach for segmentation and classification of brain stroke from noncontrast CT images

Kaya

Önal

2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Imaging is needed in stroke cases in order to understand what the type of stroke (ischemic, hemorrhagic) is, to rule out bleeding, to determine the infarct area and to plan treatment. Noncontrast CT is the primary imaging protocol used in the initial evaluation of patients with suspected stroke. As apart from studies in the literature, this paper proposes novel automated classification and segmentation approaches which are capable of extracting hemorrhage and ischemic lesions (infarcts) simultaneously from the noncontrasts brain CT images during the treatment of brain stroke patients. It is aimed to automate the detection of stroke lesions with a high accuracy rate using the U‐Net model for segmentation. In the experiments performed on the real data set, a precision value of 95.06% is obtained for the classification model. For segmentation, the IoU coefficient values from the experiments are 92.01% for hemorrhagic and 82.22% for ischemic, respectively.

show abstract

Semi‐automated infarct segmentation from follow‐up noncontrast CT scans in patients with acute ischemic stroke

Cited by 16 publications

References 19 publications

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks

Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks

Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods

A CNN transfer learning‐based approach for segmentation and classification of brain stroke from noncontrast CT images

Contact Info

Product

Resources

About