Radiomic Texture Feature Descriptor to Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal MRI

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can accurately distinguish rBT from RN. In this work, sophisticated radiomic texture features are used to distinguish rBT from RN on multimodal MRI for disease characterization. First, stochastic multiresolution radiomic descriptor that captures voxel-level textural and structural heterogeneity as well as intensity and histogram features are extracted. Subsequently, these features are used in a machine learning setting to characterize the rBT from RN from four sequences of the MRI with 155 imaging slices for 30 GBM cases (12 RN, 18 rBT). To reduce the bias in accuracy estimation our model is implemented using Leave-one-out crossvalidation (LOOCV) and stratified 5-fold cross-validation with a Random Forest classifier. Our model offers mean accuracy of 0.967 ± 0.180 for LOOCV and 0.933 ± 0.082 for stratified 5-fold cross-validation using multiresolution texture features for discrimination of rBT from RN in this study. Our findings suggest that sophisticated texture feature may offer better discrimination between rBT and RN in MRI compared to other works in the literature

Opioid Use Disorder Prediction Using Machine Learning of fMRI Data

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects’ prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development

Uncertainty Estimation in Classification of MGNT Using Radiogenomics for Glioblastoma Patients

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework for MGMT classification with uncertainty analysis utilizing imaging features extracted from multimodal magnetic resonance imaging (mMRI). The imaging features include conventional texture, volumetric, and sophisticated fractal, and multi-resolution fractal texture features. The proposed method is evaluated with publicly available BraTS-TCIA-GBM pre-operative scans and TCGA datasets with 114 patients. The experiment with 10-fold cross-validation suggests that the fractal and multi-resolution fractal texture features offer an improved prediction of MGMT status. The uncertainty analysis using an ensemble of Stochastic Gradient Langevin Boosting models along with multi-resolution fractal features offers an accuracy of 71.74% and area under the curve of 0.76. Finally, analysis shows that our proposed method with uncertainty analysis offers improved predictive performance when compared with different well-known methods in the literature

Adaptive Critic Network for Person Tracking Using 3D Skeleton Data

Analysis of human gait using 3-dimensional co-occurrence skeleton joints extracted from Lidar sensor data has been shown a viable method for predicting person identity. The co-occurrence based networks rely on the spatial changes between frames of each joint in the skeleton data sequence. Normally, this data is obtained using a Lidar skeleton extraction method to estimate these co-occurrence features from raw Lidar frames, which can be prone to incorrect joint estimations when part of the body is occluded. These datasets can also be time consuming and expensive to collect and typically offer a small number of samples for training and testing network models. The small number of samples and occlusion can cause challenges when training deep neural networks to perform real time tracking of the person in the scene. We propose preliminary results with a deep reinforcement learning actor critic network for person tracking of 3D skeleton data using a small dataset. The proposed approach can achieve an average tracking rate of 68.92±15.90% given limited examples to train the network

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types

A survey of perceptual image processing methods

Perceptual approaches have been widely used in many areas of visual information processing. This paper presents an overview of perceptual based approaches for image enhancement, segmentation and coding. The paper also provides a brief review of image quality assessment (IQA) methods, which are used to evaluate the performance of visual information processing techniques. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on few topics that have been extensively researched and developed over the past few decades. The goal is to present a perspective as broad as possible on this actively evolving domain due to relevant advances in vision research and signal processing. Therefore, for each topic, we identify the main contributions of perceptual approaches and their limitations, and outline how perceptual vision has influenced current state-of-the-art techniques in image enhancement, segmentation, coding and visual information quality assessment