New level set approach based on Parzen estimation for stroke segmentation in skull CT images

Rebouças, Elizângela de S.; Marques, Régis C. P.; Braga, Alan M.; Oliveira, Saulo A. F.; Albuquerque, Victor Hugo C. de; Filho, Pedro Pedrosa Rebouças

doi:10.1007/s00500-018-3491-4

Cited by 23 publications

(25 citation statements)

References 63 publications

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“…Our research also contributes to the advancement of computer science. Although several methods have been described in the literature to segment intracranial hemorrhage, they all suffer from some major drawbacks such as failing to balance the trade-off between accuracy and time-efficiency, sensitivity to initialization states, and lack of evaluation on CT images from multiple institutes [9][10][11][12][13][14][15][16][17] . Even the most recently developed deep learning solutions have often not been able to realize the targeted level of performance 18,19 .…”

Section: Discussionmentioning

confidence: 99%

“…Given the clinical significance, numerous attempts at developing computer-assisted automation tools for hematoma volume have been reported 13 – 16 . However, many of the techniques even till recent are either manual, semi-automatic, slow, or suffer from poor accuracy, especially for irregular hematoma shapes and sizes 13 – 19 .…”

Section: Introductionmentioning

confidence: 99%

“…Given the clinical significance, numerous attempts at developing computer-assisted automation tools for hematoma volume have been reported [13][14][15][16] . However, many of the techniques even till recent are either manual, semi-automatic, slow, or suffer from poor accuracy, especially for irregular hematoma shapes and sizes [13][14][15][16][17][18][19] . Great efforts have been geared towards building automated segmentation tools with use of artificial intelligence technologies, in particular the promising deep-learning algorithms 20,21 .…”

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confidence: 99%

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A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT

Arab

Chinda

Medvedev

et al. 2020

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This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying convolutional neural networks with deep supervision (CNN-DS) for accurate hematoma segmentation and volume quantification in computed tomography (CT) scans. Non-contrast whole-head CT scans of 55 patients with hemorrhagic stroke were used. Individual scans were standardized to 64 axial slices of 128 × 128 voxels. Each voxel was annotated independently by experienced raters, generating a binary label of hematoma versus normal brain tissue based on majority voting. The dataset was split randomly into training (n = 45) and testing (n = 10) subsets. A CNN-DS model was built applying the training data and examined using the testing data. Performance of the CNN-DS solution was compared with three previously established methods. The CNN-DS achieved a Dice coefficient score of 0.84 ± 0.06 and recall of 0.83 ± 0.07, higher than patch-wise U-Net (< 0.76). CNN-DS average running time of 0.74 ± 0.07 s was faster than PItcHPERFeCT (> 1412 s) and slice-based U-Net (> 12 s). Comparable interrater agreement rates were observed between “method-human” vs. “human–human” (Cohen’s kappa coefficients > 0.82). The fully automated CNN-DS approach demonstrated expert-level accuracy in fast segmentation and quantification of hematoma, substantially improving over previous methods. Further research is warranted to test the CNN-DS solution as a software tool in clinical settings for effective stroke management.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT

Arab

Chinda

Medvedev

et al. 2020

Sci Rep

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“…• Apply three fine-tuning techniques based on Region Growth [41], K-means clustering [42], and Parzen Window [43] to improve the edges of the region considered as a region of interest.…”

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Internet of Medical Things—Based on Deep Learning Techniques for Segmentation of Lung and Stroke Regions in CT Scans

et al. 2020

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The classification and segmentation of pathologies through intelligent systems is a significant challenge for medical image analysis and computer vision systems. Diseases, such as lung problems and strokes, have a serious effect on human health worldwide. Lung diseases are among the leading causes of death worldwide, lagging behind strokes that in 2016 became the second leading cause of death from illnesses. Computed tomography (CT) is one of the main clinical diagnostic exams, linked to Computerized Diagnostic Assistance Systems (CAD), which are becoming solutions for health technologies. In this work, we propose a method based on the health of things for the classification and segmentation of CT images of the lung and hemorrhagic stroke. The system called HTSCS -Medical Images: Health-of-Things System for the Classification and Segmentation of Medical Images, uses transfer learning between models based on deep learning combined with classical methods for fine-tuning. The proposed method obtained excellent results for the classification of hemorrhagic stroke and pulmonary regions, with values of up to 100% accuracy. The models also achieved outstanding performances for segmentation, with Accuracy above 99 % and Dice coefficient above 97% in the best cases with an average segmentation time between 0.095 and 1.7 seconds. To validate our approach, we compared our best models for the segmentation of lung and hemorrhagic stroke in CTs, with related works found in state of the art. Our method brings an innovative approach to classification and segmentation through the use of the Health of Things for different types of medical images with promising results for medical image analysis and computer vision fields.INDEX TERMS Health of things, classification and segmentation, CTs lung and stroke, transfer learning, fine-tuning. I. INTRODUCTIONVarious pathologies have a serious effect on human health worldwide, and the main ones are related to the lungs, brain, and heart. Chronic Obstructive Pulmonary Disease (COPD) is the main causes of respiratory mortality worldwide [1], andThe associate editor coordinating the review of this manuscript and approving it for publication was Victor H. Albuquerque . it was the third leading cause of death globally, according to the World Health Organization (WHO), in 2016 [2]. Also, according to the WHO, about 3.2 million deaths were caused by COPD in 2015, a total of one-twentieth of all deaths globally in that year, and over 90% of these deaths were in low and middle-income countries. Now, in 2020, 200 million people worldwide have been diagnosed with COPD, and many more are living with undiagnosed diseases [3].

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“…The paper New level set approach based on Parzen estimation for stroke segmentation in skull CT images by Rebouças et al (2018) focused on the hemorrhagic type of stroke and proposes a new approach to automatically start the level set method within the CVA region and to use a nonparametric estimation approach based on the Parzen Window to segment the CVA. The experimental results showed that the proposed method presented a superior performance compared to the other commonly used methods.…”

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confidence: 99%

Cognitive data science methods and models for engineering applications

et al. 2019

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New level set approach based on Parzen estimation for stroke segmentation in skull CT images

Cited by 23 publications

References 63 publications

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT

Internet of Medical Things—Based on Deep Learning Techniques for Segmentation of Lung and Stroke Regions in CT Scans

Cognitive data science methods and models for engineering applications

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