Spinal Cord MRI Segmentation Techniques and Algorithms: A Survey

Garg, Sheetal; Bhagyashree, S. R.

doi:10.1007/s42979-021-00618-4

Cited by 13 publications

(4 citation statements)

References 44 publications

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“…For data encryption and decryption, the security module implements two functions rsa_aes_encrypt and rsa_aes_decrypt. The Public-Key Cryptography Standards (PKCS) # 1 OPTIMAL ASYMMETRIC ENCRYPTION PADDING (OAEP) [69] padding scheme is used by the RSA's implementation, which is defined by RFC 8017. To encrypt and decrypt the keys for symmetric encryption algorithms, RSA is used.…”

Section: Securitymentioning

confidence: 99%

Automated GDPR Contract Compliance Verification Using Knowledge Graphs

et al. 2022

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In the past few years, the main research efforts regarding General Data Protection Regulation (GDPR)-compliant data sharing have been focused primarily on informed consent (one of the six GDPR lawful bases for data processing). In cases such as Business-to-Business (B2B) and Business-to-Consumer (B2C) data sharing, when consent might not be enough, many small and medium enterprises (SMEs) still depend on contracts—a GDPR basis that is often overlooked due to its complexity. The contract’s lifecycle comprises many stages (e.g., drafting, negotiation, and signing) that must be executed in compliance with GDPR. Despite the active research efforts on digital contracts, contract-based GDPR compliance and challenges such as contract interoperability have not been sufficiently elaborated on yet. Since knowledge graphs and ontologies provide interoperability and support knowledge discovery, we propose and develop a knowledge graph-based tool for GDPR contract compliance verification (CCV). It binds GDPR’s legal basis to data sharing contracts. In addition, we conducted a performance evaluation in terms of execution time and test cases to validate CCV’s correctness in determining the overhead and applicability of the proposed tool in smart city and insurance application scenarios. The evaluation results and the correctness of the CCV tool demonstrate the tool’s practicability for deployment in the real world with minimum overhead.

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Section: Securitymentioning

confidence: 99%

Automated GDPR Contract Compliance Verification Using Knowledge Graphs

et al. 2022

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“…On the other hand, a dataset with about 1000 images would still be considered modest, and the larger the deep-learning model is, the more it is in need of larger image datasets. Thus, our proposed solution based on a light CNN model, such as MobileUNetV3, is well justified considering the rather modest and limited dataset size of 1092 available SCGM magnetic resonance images [18].…”

Section: Dataset Description and Pre-processingmentioning

confidence: 99%

MobileUNetV3—A Combined UNet and MobileNetV3 Architecture for Spinal Cord Gray Matter Segmentation

2022

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The inspection of gray matter (GM) tissue of the human spinal cord is a valuable tool for the diagnosis of a wide range of neurological disorders. Thus, the detection and segmentation of GM regions in magnetic resonance images (MRIs) is an important task when studying the spinal cord and its related medical conditions. This work proposes a new method for the segmentation of GM tissue in spinal cord MRIs based on deep convolutional neural network (CNNs) techniques. Our proposed method, called MobileUNetV3, has a UNet-like architecture, with the MobileNetV3 model being used as a pre-trained encoder. MobileNetV3 is light-weight and yields high accuracy compared with many other CNN architectures of similar size. It is composed of a series of blocks, which produce feature maps optimized using residual connections and squeeze-and-excitation modules. We carefully added a set of upsampling layers and skip connections to MobileNetV3 in order to build an effective UNet-like model for image segmentation. To illustrate the capabilities of the proposed method, we tested it on the spinal cord gray matter segmentation challenge dataset and compared it to a number of recent state-of-the-art methods. We obtained results that outperformed seven methods with respect to five evaluation metrics comprising the dice similarity coefficient (0.87), Jaccard index (0.78), sensitivity (87.20%), specificity (99.90%), and precision (87.96%). Based on these highly competitive results, MobileUNetV3 is an effective deep-learning model for the segmentation of GM MRIs in the spinal cord.

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“…The security of distributed systems is traditionally proposed to be provided using asymmetric ciphers [33], such as RSA [34]. There are many works devoted to this topic [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Fast Operation of Determining the Sign of a Number in RNS Using the Akushsky Core Function

et al. 2023

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This article presents a study related to increasing the performance of distributed computing systems. The essence of fog computing lies in the use of so-called edge devices. These devices are low-power, so they are extremely sensitive to the computational complexity of the methods used. This article is aimed at improving the efficiency of calculations while maintaining an appropriate level of reliability by applying the methods of the Residue Number System (RNS). We are investigating methods for determining the sign of a number in the RNS based on the core function in order to develop a new, fast method. As a result, a fast method for determining the sign of a number based on the Akushsky core function, using approximate calculations, is obtained. Thus, in the course of this article, a study of methods for ensuring reliability in distributed computing is conducted. A fast method for determining the sign of a number in the RNS based on the core function using approximate calculations is also proposed. This result is interesting from the point of view of nebulous calculations, since it allows maintaining high reliability of a distributed system of edge devices with a slight increase in the computational complexity of non-modular operations.

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Spinal Cord MRI Segmentation Techniques and Algorithms: A Survey

Cited by 13 publications

References 44 publications

Automated GDPR Contract Compliance Verification Using Knowledge Graphs

Automated GDPR Contract Compliance Verification Using Knowledge Graphs

MobileUNetV3—A Combined UNet and MobileNetV3 Architecture for Spinal Cord Gray Matter Segmentation

Fast Operation of Determining the Sign of a Number in RNS Using the Akushsky Core Function

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