Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization

Elsayed, Ashraf

doi:10.5772/50019

Cited by 5 publications

(4 citation statements)

References 46 publications

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“…The authors advocated the superiority of their model compared to previous machine learning studies, as the overall preprocessing effort was sharply reduced. However, the use of non-segmented whole-brain scans may lead to additional classification bias, as DNNs might learn to accomplish their classification task by relying on features (e.g., anatomical location and laterality) determined by unbalanced and heterogeneous training sets instead of clinically related radiological differences, hence limiting the general applicability of the model ( 37 ). On the contrary, lesion segmentation partitions each selected slice into a coherent region of interest (ROI) that is extracted from the background and individually processed to acquire overall lesion’s characteristics (either ROI or boundaries).…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

et al. 2022

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BackgroundNeuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) remains challenging in specific cases showing similar appearances or atypical features. Overall, advanced MRI protocols have high diagnostic reliability, but their limited worldwide availability, coupled with the overlapping of specific neuroimaging features among tumor subgroups, represent significant drawbacks and entail disparities in the planning and management of these oncological patients.ObjectiveTo evaluate the classification performance metrics of a deep learning algorithm trained on T1-weighted gadolinium-enhanced (T1Gd) MRI scans of glioblastomas, atypical PCNSLs and BMs.Materials and MethodsWe enrolled 121 patients (glioblastoma: n=47; PCNSL: n=37; BM: n=37) who had undergone preoperative T1Gd-MRI and histopathological confirmation. Each lesion was segmented, and all ROIs were exported in a DICOM dataset. The patient cohort was then split in a training and hold-out test sets following a 70/30 ratio. A Resnet101 model, a deep neural network (DNN), was trained on the training set and validated on the hold-out test set to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans.ResultsThe DNN achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.98; 95%CI: 0.95 - 1.00) and glioblastomas (AUC: 0.90; 95%CI: 0.81 - 0.97) and moderate ability in differentiating BMs (AUC: 0.81; 95%CI: 0.70 - 0.95). This performance may allow clinicians to correctly identify patients eligible for lesion biopsy or surgical resection.ConclusionWe trained and internally validated a deep learning model able to reliably differentiate ambiguous cases of PCNSLs, glioblastoma and BMs by means of T1Gd-MRI. The proposed predictive model may provide a low-cost, easily-accessible and high-speed decision-making support for eligibility to diagnostic brain biopsy or maximal tumor resection in atypical cases.

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Section: Discussionmentioning

confidence: 99%

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

et al. 2022

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“…Computer vision (CV) models based on CNNs have become popular for solving the Alzheimer's disease classification problem. Frequently, medical imaging processing requires preprocessing to capture regions of interest (ROI) (Elsayed et al, 2012). This can be done manually or using signal processing techniques, such as the Hough transform (Duda and Hart, 1972) and the scale-invariant feature transform (SIFT) (Lowe, 1999).…”

Section: Methodologies For Ad Imaging Classification Cnnmentioning

confidence: 99%

“…Convolutional layers contain hyperparameters that can be optimized, such as padding (Dwarampudi and Reddy, 2019).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

Logan

Williams

Silva

et al. 2021

Front. Aging Neurosci.

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Recent advancements in deep learning (DL) have made possible new methodologies for analyzing massive datasets with intriguing implications in healthcare. Convolutional neural networks (CNN), which have proven to be successful supervised algorithms for classifying imaging data, are of particular interest in the neuroscience community for their utility in the classification of Alzheimer’s disease (AD). AD is the leading cause of dementia in the aging population. There remains a critical unmet need for early detection of AD pathogenesis based on non-invasive neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). In this comprehensive review, we explore potential interdisciplinary approaches for early detection and provide insight into recent advances on AD classification using 3D CNN architectures for multi-modal PET/MRI data. We also consider the application of generative adversarial networks (GANs) to overcome pitfalls associated with limited data. Finally, we discuss increasing the robustness of CNNs by combining them with ensemble learning (EL).

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“…Most of the MRI‐based methods to diagnose the prevalence of AD are categorized as follows: Multivariate approaches—The methods which use whole brain image to find disease patterns in brain ROI‐based approaches—The methods which use region/volume of interest‐based detection …”

Section: State Of the Artmentioning

confidence: 99%

A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease

Mishra

Beheshti²,

Khanna

2018

Int J Imaging Syst Tech

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Identification of dominant imaging biomarkers is important for early detection of Alzheimer's disease (AD) and to improve diagnostic accuracy. This work proposes a novel automatic computer aided diagnosis (CAD) system working on region selection framework. Voxel based morphometry and tissue segmentation is performed to get gray matter (GM) images. These pre-processed images are anatomized to get 116 regions of brain using a standard automated anatomical labeling atlas. The proposed region selection algorithm identifies the most relevant brain regions out of 116 regions to discriminate AD and healthy control (HC) subjects. Volumetric features (standard deviation, skewness, kurtosis, energy, and shannon entropy) are extracted and random feature selection is performed to get the most discriminating regions to classify AD from HC. Supervised classification algorithms are used to explore and validate the proposed methodology. Experimental results indicate that the performance of the proposed system competes well with the state-of-the-art techniques.

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Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization

Cited by 5 publications

References 46 publications

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease

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