A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

Khosravanian, Asieh; Rahmanimanesh, Mohammad; Keshavarzi, Parviz; Mozaffari, Saeed

doi:10.1016/j.jneumeth.2021.109091

Cited by 12 publications

(4 citation statements)

References 44 publications

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“…The Khairandish et al 42 suggested employing a Multi‐scale convolutional neural network (MCCNN) for feature extraction and adding. Verma et al 45 created the original 2.5D U‐Net network, and then Khosravanian et al 44 added dilated convolutional feature pyramids to the 3D Fully Convolutional Network (FCN). By comparing the data, it can be seen that the suggested strategy yields the highest Dice coefficient value for the tumor core region and the second‐highest Dice value for the augmenting tumor region.…”

Section: Experimental Detail and Results Analysismentioning

confidence: 99%

Multiencoder‐based federated intelligent deep learning model for brain tumor segmentation

Soni,

Singh,

Singh

et al. 2023

Int J Imaging Syst Tech

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Glioma, a primary tumor derived from brain glial cells, is around 45% of all intracranial tumors. Magnetic resonance imaging's (MRI's) precise glioma segmentation is crucial for clinical purposes. This article presents a novel automatic brain tumor segmentation approach based on a multi‐encoder‐based federated intelligent deep learning framework. The suggested method uses a U‐shaped network design that multiplies the single contraction path into several paths to explore semantic information modalities deeply. The basic convolutional layer uses an Inception module and dilated convolutions to extract multi‐scale features from the images using artificial intelligent. To emphasize segmentation‐related information while ignoring redundant channel dimension information and improving the accuracy of network segmentation, lightweight channel attention efficient channel attention (ECA) modules are inserted into the bottleneck layer and decoder. The collection of data for the 2018 Brain Tumor Segmentation Challenge (BraTS 2018) is used to test the effectiveness of the suggested structure, and the findings indicate that the growth core, for the entire tumor and the augmented tumor regions, respectively, the average Dice coefficients are 0.880, 0.784, and 0.757. These findings support the proposed algorithm's ability to accurately and successfully segregate multimodal MRI brain tumors.

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Section: Experimental Detail and Results Analysismentioning

confidence: 99%

Multiencoder‐based federated intelligent deep learning model for brain tumor segmentation

Soni,

Singh,

Singh

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Yuvaraj et al [15] detect brain tumor by investigation based on multi-perspective scaling convolutional neural networks model. Khosravanian et al [17,18] satisfied the results for glioma brain tumor segmentation due to super pixel fuzzy clustering and gives accurate segmentation results. The author also proposed a region-based level set technique to segment image using intensity inhomogeneity.…”

Section: Related Workmentioning

confidence: 93%

Interpolation Method for Identification of Brain Tumor from Magnetic Resonance Images

Singh¹,

Saxena²

2023

IJEM

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During the past years, it is observed from the literature that, identification of the brain tumor identification in human being is gaining popularity. Diagnosing any disease without manual interaction with great accuracy makes computer science research more demanding, therefore, the present work is related to identify the tumor clots in the affected patients. For this purpose, a well-known Safdarganj Hospital, New Delhi, India is consulted and 2165 Magnetic Resonance Images (MRI) of a single patient are collected through scanning, and interpolation technique of numerical method used to identify the accurate position of the brain tumor. A system model is developed and implemented by the use of Python programming language and MATLAB for the identification of affected areas in the form of a contour of a patient. The desired accuracy and specificity are evaluated using the computed results and also presented in the form of graphs.

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“…In this approach processed the input MR images in 3D using three different processing pathways which segmented and classified the three types of Brain Tumors: glioma, meningioma, and pituitary tumor without the need to remove the skull and vertebral column parts. In order to segment the images with intensity inhomogeneity in MRI scans for brain tumors, a unique region-based level set approach is developed in Asieh Khosravanian et al [66]. In order to do this, the inhomogeneous zones are first represented as Gaussian distributions with different means and variances, and then moved into a new domain, where each region's Gaussian intensity distribution is kept but with improved separation.…”

Section: Recent Approaches Of Brain Neoplasm Segmentation Techniquesmentioning

confidence: 99%

Recent Segmentation Trends of MRI-Based Brain Neoplasm: A Review

Siddiqui,

Qadri,

Tayyab

et al. 2023

JAET

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A significant increase in medical-related cases of brain neoplasm has been noticed in the past few years affecting not only adults but children as well. Brain neoplasm segmentation isolated the different abnormal brain tissues from normal brain tissues which are complicated tasks in medical image examination. But simultaneously it plays a crucial role in time diagnosis which not only improved the treatment possibilities but also increased the rate of survival of the patients because a brain neoplasm is a treatable kind of cancer if diagnose well on time. Magnetic resonance imaging (MRI) based segmentation are a more focused, attractive, and attentive study area in recent few years because MRI is noninvasive imaging, safe, and cost-effective. Detection of brain neoplasm using the manual procedure of segmentation is an extremely difficult, time-consuming, expensive, and individual task because large data of MRI images are produced in clinical practice which may delay the diagnosis. This increases the practical significance of the automatic segmentation techniques but due to brain neoplasm being extremely unpredictable concerning position, appearance, type, and size improving the methods for state-of-the-art segmentation process remain a complex task. To segment the brain neoplasm accurately, automatic segmentation is a solution with better performance. The goal of this paper provides an organized literature survey for recent MRI-based automatic brain neoplasm segmentation techniques with the modern participation of several researchers which helps new researchers in exploring future directions. There are several current surveys focused on traditional methods, but this paper focuses on recent trends including machine learning techniques accompanied by transfer learning, deep learning, neural network, and hybridization. Moreover, this survey also presents the findings and limitations of each article which show the effectiveness of the proposed work. Finally, this survey, found that after over two decades of research, the novel methods for segmenting brain neoplasms using computer-aided techniques are becoming more and more refined and becoming closer to being used often in clinical settings but in terms of computing complexity and memory consumption, these approaches lag.

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A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

Cited by 12 publications

References 44 publications

Multiencoder‐based federated intelligent deep learning model for brain tumor segmentation

Multiencoder‐based federated intelligent deep learning model for brain tumor segmentation

Interpolation Method for Identification of Brain Tumor from Magnetic Resonance Images

Recent Segmentation Trends of MRI-Based Brain Neoplasm: A Review

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