Ischemic stroke lesion segmentation using stacked sparse autoencoder

Gorla, Praveen; Agrawal, Anita; Sundaram, P. Kalyana; Sardesai, Sanjay

doi:10.1016/j.compbiomed.2018.05.027

Cited by 58 publications

(26 citation statements)

References 29 publications

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“…In particular, supervised learning with deep convolutional neural networks (DCNNs) is being utilized to solve many problems in MRI. Given training pairs of MR images, DCNNs have been trained to perform many challenging and clinically useful tasks, including image reconstruction of undersampled data, segmentation of structures, and synthesis of images with higher resolution or images from a different modality . Many training pairs—examples of the input and target images—are required to train the network, which effectively learns the mapping relationship between the input and target domains.…”

Section: Introductionmentioning

confidence: 99%

Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Johnson

Drangova

2019

Magnetic Resonance in Med

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westgrid.ca) and Compute Canada Calcul Canada (www.computecanada.ca).Purpose: Subject motion in MRI remains an unsolved problem; motion during image acquisition may cause blurring and artifacts that severely degrade image quality. In this work, we approach motion correction as an image-to-image translation problem, which refers to the approach of training a deep neural network to predict an image in 1 domain from an image in another domain. Specifically, the purpose of this work was to develop and train a conditional generative adversarial network to predict artifact-free brain images from motion-corrupted data.Methods: An open source MRI data set comprising T 2 *-weighted, FLASH magnitude, and phase brain images for 53 patients was used to generate complex image data for motion simulation. To simulate rigid motion, rotations and translations were applied to the image data based on randomly generated motion profiles. A conditional generative adversarial network, comprising a generator and discriminator networks, was trained using the motion-corrupted and corresponding ground truth (original) images as training pairs. Results: The images predicted by the conditional generative adversarial network have improved image quality compared to the motion-corrupted images. The mean absolute error between the motion-corrupted and ground-truth images of the test set was 16.4% of the image mean value, whereas the mean absolute error between the conditional generative adversarial network-predicted and ground-truth images was 10.8% The network output also demonstrated improved peak SNR and structural similarity index for all test-set images. Conclusion: The images predicted by the conditional generative adversarial network have quantitatively and qualitatively improved image quality compared to the motioncorrupted images. K E Y W O R D S conditional generative adversarial networks, convolutional neural networks, deep learning, motion correction, MRI

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Section: Introductionmentioning

confidence: 99%

Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Johnson

Drangova

2019

Magnetic Resonance in Med

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“…It allowed us to detect more deep lesions and provided better segmentation of periventricular lesion boundaries. Praveen et al 105 showed that a deep architecture is using SAE layers. The experimental results showed that the proposed approach significantly outperforms the state-of-the-art methods in terms of precision, DC, and recall.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for brain diseases: A systematic review

et al. 2020

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Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using “artificial intelligence” and “brain” as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

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“…In the case of processing stroke with CNNs, the featurization of the images is a key application [75,124] and depends on the signal-to-noise ratio in the image, which can be improved by target identification via segmentation to select regions of interest [124]. According to Praveen et al, [193], a CNN learns to discriminate local features and returns better performance than hand-crafted features.…”

Section: Feature Extractionmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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Ischemic stroke lesion segmentation using stacked sparse autoencoder

Cited by 58 publications

References 29 publications

Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Artificial intelligence for brain diseases: A systematic review

MR Images, Brain Lesions, and Deep Learning

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