Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making

Hemanth, D. Jude; Rajinikanth, V.; Rao, V. Seshagiri; Mishra, Samaresh; Hannon, Naeem M. S.; Rajangam, Vijayarajan; Arunmozhi, S.

doi:10.1007/s12065-020-00551-0

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…+ere are many challenges in automatic segmentation problems, and their use in practice is quite limited due to the difficulty of correctly validating the predictions made by these tools. In this memory, the effectiveness of different segmentation methods for this type of image will be studied, proposing the use of convolutional neural networks for the segmentation of lesions and comparing them with traditional segmentation methods, analyzing the advantages and disadvantages that each of these methods they bring into practice [5].…”

Section: Introductionmentioning

confidence: 99%

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Hamad

Jaber

Ahmed

et al. 2022

Advances in Materials Science and Engineering

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Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis. Experts usually perform this detection process manually, but there is interest in automating it to speed up the diagnosis and monitoring of this disease. A variety of automatic image segmentation methods have been proposed to quickly detect these lesions. A Gaussian Mixture Model is first constructed to identify outliers in each image. Then, using a set of rules based on expert knowledge of multiple sclerosis lesions, those outliers of the model that do not match the lesions' characteristics are discarded. Furthermore, segmented lesions usually correspond to gray matter-rich brain regions. In some cases, false positives can be detected, but the rules used cannot eliminate all errors without jeopardizing the segmentation’s quality. The second method involves training a convolutional neural network (CNN) that can segment lesions based on a set of training images. This technique can learn a set of filters that, when applied to small sections of an image called “patches,” produce a set of characteristics that can be used to classify each voxel of the image as a lesion or healthy tissue. On the other hand, the results show that the networks are capable of producing results in the worked database comparable to those produced by the algorithms in the literature.

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Section: Introductionmentioning

confidence: 99%

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Hamad

Jaber

Ahmed

et al. 2022

Advances in Materials Science and Engineering

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“…N OW deep learning has been used in many fields, especially in health care to improve medical diagnosis [1]. Constructing deep learning models often entails large quantities of data.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Image Gradient Inversion Against Federated Learning

Li¹,

Wang²,

Chen³

et al. 2022

Preprint

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In order to preserve data privacy while fully utilizing data from different owners, federated learning is believed to be a promising approach in recent years. However, aiming at federated learning in the image domain, gradient inversion techniques can reconstruct the input images on pixel-level only by leaked gradients, without accessing the raw data, which makes federated learning vulnerable to the attacks. In this paper, we review the latest advances of image gradient inversion techniques and evaluate the impact of them to federated learning from the attack perspective. We use eight models and four datasets to evaluate the current gradient inversion techniques, comparing the attack performance as well as the time consumption. Furthermore, we shed light on some important and interesting directions of gradient inversion against federated learning.<br>

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“…Brain signals and brain images are widely used in clinical diagnosis of cerebral apoplexy. Considering its accuracy and multimode features, some scholars relied on principal component analysis (PCA) to develop a pixel-level image segmentation method, in which image thresholding is performed through cuckoo search and Tsallis entropy [24,25]. The results show that the pixel-level fusion improves the clinical disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation for Review of Cerebral Apoplexy

Jiang¹

2021

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Brain computed tomography (CT) provides a medical imaging tool for reviewing cerebral apoplexy. It is of strong clinical significance to study the key techniques for lesion segmentation and feature selection of cerebral apoplexy. Most of the previous research fail to fully utilized the other prior information, or apply to the changing feature analysis on multiple lesion images generated in the rehabilitation process. Therefore, this paper aims to develop an image segmentation method for review of cerebral apoplexy. Based on the correlation between image series, the authors proposed a segmentation method for CT images of cerebral apoplexy, and developed a way to extract and select the changing lesion features, which assists with the diagnosis of cerebral apoplexy rehabilitation. The image segmentation and feature selection results were obtained through experiments, revealing the effectiveness of our method.

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Image fusion practice to improve the ischemic-stroke-lesion detection for efficient clinical decision making

Cited by 9 publications

References 28 publications

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging

A Survey of Image Gradient Inversion Against Federated Learning

Image Segmentation for Review of Cerebral Apoplexy

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