Ischemic Stroke Lesion Prediction in CT Perfusion Scans Using Multiple Parallel U-Nets Following by a Pixel-Level Classifier

Soltanpour, Mohsen; Greiner, Russell; Boulanger, Pierre; Buck, Brian

doi:10.1109/bibe.2019.00179

Cited by 18 publications

(6 citation statements)

References 25 publications

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“…Abd-Ellah et al [92] arranged two parallel U-nets and aggregated the results for improved segmentation accuracy. Soltanpour et al [93] implemented four parallel U-nets with each segmenting a different CT map and then merging the results. A halfway point can be achieved by parallel encoders, which allow for better extraction of features [94]- [96].…”

Section: J Ensemble U-netmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: J Ensemble U-netmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…However, many studies classify either hemorrhage or ischemia. In some studies, CT segmentation applications of the stroke-related region are carried out with different deep-learning models [13,17,[41][42][43][44]. There are also studies that use MR images, which are acquired over a longer duration compared to CT images, to perform stroke classification [45][46][47][48][49][50][51][52].…”

Section: Resultsmentioning

confidence: 99%

Performance Evaluation of Different Deep Learning Models for Classifying Ischemic, Hemorrhagic, and Normal Computed Tomography Images: Transfer Learning Approaches

Altıntaş,

Öziç

2024

KONJES

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A stroke is a case of damage to a brain area due to a sudden decrease or complete cessation of blood flow to the brain. The interruption or reduction of the transportation of oxygen and nutrients through the bloodstream causes damage to brain tissues. Thus, motor or sensory impairments occur in the body part controlled by the affected area of the brain. There are primarily two main types of strokes: ischemic and hemorrhagic. When a patient is suspected of having a stroke, a computed tomography scan is performed to identify any tissue damage and facilitate prompt intervention quickly. Early intervention can prevent the patient from being permanently disabled throughout their lifetime. This study classified ischemic, hemorrhage, and normal computed tomography images taken from international databases as open source with AlexNet, ResNet50, GoogleNet, InceptionV3, ShuffleNet, and SqueezeNet deep learning models using transfer learning approach. The data were divided into 80% training and 20% testing, and evaluation metrics were calculated by five-fold cross-validation. The best performance results for the three-class output were obtained with AlexNet as 0.9086±0.02 precision, 0.9097±0.02 sensitivity, 0.9091±0.02 F1 score, 0.9089±0.02 accuracy. The average area under curve values was obtained with AlexNet 0.9920±0.005 for ischemia, 0.9828±0.008 for hemorrhage, and 0.9686±0.012 for normal.

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“…Soltanpour et al developed two models using modified U-Nets taking in input a set of PMs derived from CTP. [28,29]. However, a general problem with these methods is the use of heavily pre-processed information made for visual interpretation (i.e.…”

Section: Introductionmentioning

confidence: 99%

Exploiting 4D CT Perfusion for segmenting infarcted areas in patients with suspected acute ischemic stroke

Tomasetti¹,

Engan²,

Høllesli³

et al. 2023

Preprint

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Precise and fast prediction methods for ischemic areas (core and penumbra) in acute ischemic stroke (AIS) patients are of significant clinical interest: they play an essential role in improving diagnosis and treatment planning. Computed Tomography (CT) scan is one of the primary modalities for early assessment in patients with suspected AIS. CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions. Current automatic segmentation methods for CTP mostly use already processed 3D color maps conventionally used for visual assessment by radiologists as input. Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time input, where the spatial information over the volume is ignored. In this paper, we investigate different methods to utilize the entire 4D CTP as input to fully exploit the spatio-temporal information. This leads us to propose a novel 4D convolution layer. Our comprehensive experiments on a local dataset comprised of 152 patients divided into three groups show that our proposed models generate more precise results than other methods explored. A Dice Coefficient of 0.70 and 0.45 is achieved for All authors are with the BioMedical Data analysis group (https://www.uis.no/en/ bmdlab)

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Ischemic Stroke Lesion Prediction in CT Perfusion Scans Using Multiple Parallel U-Nets Following by a Pixel-Level Classifier

Cited by 18 publications

References 25 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Performance Evaluation of Different Deep Learning Models for Classifying Ischemic, Hemorrhagic, and Normal Computed Tomography Images: Transfer Learning Approaches

Exploiting 4D CT Perfusion for segmenting infarcted areas in patients with suspected acute ischemic stroke

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