Review of advanced computational approaches on multiple sclerosis segmentation and classification

Shanmuganathan, Manimurugan; Almutairi, Saad; Aborokbah, Majed; Ganesan, Subramaniam; Ramachandran, Vasukidevi

doi:10.1049/iet-spr.2019.0543

Cited by 42 publications

(12 citation statements)

References 58 publications

(77 reference statements)

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“…Therefore, prior studies have attempted to build automated segmentation methods for cortical tubers, mostly adapted from focal cortical dysplasia (FCD) detection models. Conventional methods for detecting FCD or TSC tubers include a morphometric MRI analysis or surface‐based morphometry algorithms, which are based on rule‐based feature extractions 30,31 …”

Section: Discussionmentioning

confidence: 99%

Convolutional neural network‐aided tuber segmentation in tuberous sclerosis complex patients correlates with electroencephalogram

et al. 2022

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Objective: One of the clinical hallmarks of tuberous sclerosis complex (TSC) is radiologically identified cortical tubers, which are present in most patients.Intractable epilepsy may require surgery, often involving invasive diagnostic procedures such as intracranial electroencephalography (EEG). Identifying the location of the dominant tuber responsible for generating epileptic activities is a critical issue. However, the link between cortical tubers and epileptogenesis is poorly understood. Given this, we hypothesized that tuber voxel intensity may be an indicator of the dominant epileptogenic tuber. Also, via tuber segmentation based on deep learning, we explored whether an automatic quantification of the tuber burden is feasible. Methods:We annotated tubers from structural magnetic resonance images across 29 TSC subjects, summarized tuber statistics in eight brain lobes, and determined suspected epileptogenic lobes from the same group using EEG monitoring data. Then, logistic regression analyses were performed to demonstrate the linkage between the statistics of cortical tuber and the epileptogenic zones. Furthermore, we tested the ability of a neural network to identify and quantify tuber burden.Results: Logistic regression analyses showed that the volume and count of tubers per lobe, not the mean or variance of tuber voxel intensity, were positively correlated with electrophysiological data. In 47.6% of subjects, the lobe with the largest tuber volume concurred with the epileptic brain activity. A neural network model on the test dataset showed a sensitivity of .83 for localizing individual tubers. The predicted masks from the model correlated highly with the neurologist labels, and thus may be a useful tool for determining tuber burden and searching for the epileptogenic zone.

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Section: Discussionmentioning

confidence: 99%

Convolutional neural network‐aided tuber segmentation in tuberous sclerosis complex patients correlates with electroencephalogram

et al. 2022

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“…This microcontroller has the ability to perform activities related to WIFI and is therefore widely used as a WIFI unit [20]. There are many types of ESP8266 units available ranging from ESP8266-01 to ESP8266-12 [26][27][28][29][30]. What we use in the tutorial is ESP8266-01 because it is cheaper and readily available.…”

Section: Bl O C K D Ia G R a Mmentioning

confidence: 99%

Certain Investigations on IoT system for COVID-19

Jaafari

Alhasani

alghosn

et al. 2020

2020 International Conference on Computing and Information Technology (ICCIT-1441)

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Corona Viruses are a group of viruses that can cause diseases such as colds, severe acute respiratory syndrome (SARS) and the Middle East Respiratory Syndrome (MERS). A new type of corona virus has been discovered after it was identified as the cause of the spread of one of the diseases that started in China in 2019. This disease is considered one of the most dangerous things in the world, which directly affected many areas and led to high financial losses. The risk in this disease lies in its wide spread and the difficulty in dealing with and responding to it. So remote control technologies is the best solution for monitoring the patient's condition and monitoring the change of symptoms. The internet of thing one of modern technology which aims to shares files, software, programs and other tools to allow user to uses the devises with each other to apply the communication between them. it includes many devices communication between them by intelligent decisions. building modern IOT system based smart devices and sensors is the best solution to detect the patient of COVID-19 at real time. The study shows how effective device to detect of COVID-19 patient in IOT system.

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“…A comprehensive review is very important to help future generations design better automatic segmentation models based on the predecessors. In the past few years, there have also been related reviews (Danelakis et al, 2018;Kaur et al, 2020;Shanmuganathan et al, 2020) published. Danelakis et al (2018) reviews the methods of automatically segmenting MS lesions and pointed out that MRI data acquisition and the injection of the contrast medium during data acquisition are great challenges in the future.…”

Section: Introductionmentioning

confidence: 99%

“…Kaur et al (2020) reviews the state-of-the-art methods by 2019 and lists the future directions obtained from these methods for future reference. Shanmuganathan et al (2020) reviews the classification and segmentation methods of MS lesions and compares the classification and segmentation methods separately. Their comparison of various strategies shows that the segmentation methods based on deep learning achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

“…Although the previous reviews (Danelakis et al, 2018;Kaur et al, 2020;Shanmuganathan et al, 2020) have done a great job, there are still no reviews that give a comprehensive overview of the deep learning-based automatic segmentation methods individually which achieve excellent performance. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review.…”

Section: Introductionmentioning

confidence: 99%

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Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

Zeng

Liu

et al. 2020

Front. Neuroinform.

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In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.

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Review of advanced computational approaches on multiple sclerosis segmentation and classification

Cited by 42 publications

References 58 publications

Convolutional neural network‐aided tuber segmentation in tuberous sclerosis complex patients correlates with electroencephalogram

Convolutional neural network‐aided tuber segmentation in tuberous sclerosis complex patients correlates with electroencephalogram

Certain Investigations on IoT system for COVID-19

Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

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