scholarly journals Predicting pediatric optic pathway glioma progression using advanced magnetic resonance image analysis and machine learning

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Jared M Pisapia ◽  
Hamed Akbari ◽  
Martin Rozycki ◽  
Jayesh P Thawani ◽  
Phillip B Storm ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
White Matter ◽  
Magnetic Resonance ◽  
Predictive Model ◽  
Diffusion Tensor ◽  
Machine Learning Techniques ◽  
Optic Pathway Glioma ◽  
Low Grade ◽  
Optic Pathway

Abstract Background Optic pathway gliomas (OPGs) are low-grade tumors of the white matter of the visual system with a highly variable clinical course. The aim of the study was to generate a magnetic resonance imaging (MRI)-based predictive model of OPG tumor progression using advanced image analysis and machine learning techniques. Methods We performed a retrospective case–control study of OPG patients managed between 2009 and 2015 at an academic children’s hospital. Progression was defined as radiographic tumor growth or vision decline. To generate the model, optic nerves were manually highlighted and optic radiations (ORs) were segmented using diffusion tractography tools. For each patient, intensity distributions were obtained from within the segmented regions on all imaging sequences, including derivatives of diffusion tensor imaging (DTI). A machine learning algorithm determined the combination of features most predictive of progression. Results Nineteen OPG patients with progression were matched to 19 OPG patients without progression. The mean time between most recent follow-up and most recently analyzed MRI was 3.5 ± 1.7 years. Eighty-three MRI studies and 532 extracted features were included. The predictive model achieved an accuracy of 86%, sensitivity of 89%, and specificity of 81%. Fractional anisotropy of the ORs was among the most predictive features (area under the curve 0.83, P < 0.05). Conclusions Our findings show that image analysis and machine learning can be applied to OPGs to generate a MRI-based predictive model with high accuracy. As OPGs grow along the visual pathway, the most predictive features relate to white matter changes as detected by DTI, especially within ORs.

Magnetic Resonance Image Analysis for Brain CAD Systems with Machine Learning

2012 ◽  
pp. 258-296 ◽  
Author(s):  
Hidetaka Arimura ◽  
Chiaki Tokunaga ◽  
Yasuo Yamashita ◽  
Jumpei Kuwazuru
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Magnetic Resonance ◽  
Image Feature ◽  
Machine Learning Techniques ◽  
Brain Diseases ◽  
Cad Systems ◽  
Unruptured Aneurysms ◽  
Development Cycle ◽  
Learning Techniques

This chapter describes the image analysis for brain Computer-Aided Diagnosis (CAD) systems with machine learning techniques, which could assist radiologists in the detection of such brain diseases as asymptomatic unruptured aneurysms, Alzheimer’s Disease (AD), vascular dementia, and Multiple Sclerosis (MS) by magnetic resonance imaging. Image analysis in CAD systems consists of image enhancement, initial detection, and image feature extraction, including segmentation. In addition, the authors review the classification of true and false positives using machine learning techniques, as well as the evaluation methods and development cycle for CAD systems.

Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging

2007 ◽  
Vol 107 (3) ◽  
pp. 488-494 ◽  
Author(s):  
Jeffrey I. Berman ◽  
Mitchel S. Berger ◽  
Sungwon Chung ◽  
Srikantan S. Nagarajan ◽  
Roland G. Henry
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Bipolar Electrode ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Motor Pathway ◽  
Magnetic Source ◽  
Subcortical Stimulation

Object Resecting brain tumors involves the risk of damaging the descending motor pathway. Diffusion tensor (DT)–imaged fiber tracking is a noninvasive magnetic resonance (MR) technique that can delineate the subcortical course of the motor pathway. The goal of this study was to use intraoperative subcortical stimulation mapping of the motor tract and magnetic source imaging to validate the utility of DT-imaged fiber tracking as a tool for presurgical planning. Methods Diffusion tensor-imaged fiber tracks of the motor tract were generated preoperatively in nine patients with gliomas. A mask of the resultant fiber tracks was overlaid on high-resolution T1- and T2-weighted anatomical MR images and used for stereotactic surgical navigation. Magnetic source imaging was performed in seven of the patients to identify functional somatosensory cortices. During resection, subcortical stimulation mapping of the motor pathway was performed within the white matter using a bipolar electrode. Results A total of 16 subcortical motor stimulations were stereotactically identified in nine patients. The mean distance between the stimulation sites and the DT-imaged fiber tracks was 8.7 ±3.1 mm (±standard deviation). The measured distance between subcortical stimulation sites and DT-imaged fiber tracks combines tracking technique errors and all errors encountered with stereotactic navigation. Conclusions Fiber tracks delineated using DT imaging can be used to identify the motor tract in deep white matter and define a safety margin around the tract.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

scholarly journals Diffusion tensor magnetic resonance imaging of optic pathway in cases of suprasellar masses

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Nada Ashraf Abd El-Hady ◽  
Mohammad Mahmoud Alhousini Alashwah ◽  
Haitham Haroun Emam ◽  
Ashraf Mohamed Farid Rahil
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Optic Pathway ◽  
Resonance Imaging

Optic pathway glioma infiltrating into somatostatinergic pathways in a young boy with gigantism

1994 ◽  
Vol 81 (4) ◽  
pp. 595-600 ◽  
Author(s):  
Thomas J. Manski ◽  
Charles S. Ha worth ◽  
Bertrand J. Duval-Arnould ◽  
Elisabeth J. Rushing
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Optic Pathway Glioma ◽  
Optic Pathway ◽  
Resonance Imaging ◽  
Content Type ◽  
Hyperintense Signal ◽  
Immunocytochemical Studies ◽  
Stereotactic Biopsies ◽  
Fine Print

✓ The authors report gigantism in a 16-month-old boy with an extensive optic pathway glioma infiltrating into somatostatinergic pathways, as revealed by magnetic resonance imaging and immunocytochemical studies. Stereotactic biopsies of areas showing hyperintense signal abnormalities on T2-weighted images in and adjacent to the involved visual pathways provided rarely obtained histological correlation of such areas. The patient received chemotherapy, which resulted in reduction of size and signal intensity of the tumor and stabilization of vision and growth velocity.

scholarly journals Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 157
Author(s):  
Daniel Santos ◽  
José Saias ◽  
Paulo Quaresma ◽  
Vítor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Road Accident ◽  
Influential Factors ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Learning Approaches ◽  
Road Accidents ◽  
Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

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