Radiomics Based Differentiation of Glioblastoma and Metastatic Disease: Impact of Different T1-Contrast Enhanced Sequences on Radiomic Features and Model Performance.

Background And Purpose: To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and brain metastases (BM) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences.

Materials And Methods: T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 BM) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes).

Acetylsalicylic acid use and development of cardiac allograft vasculopathy: A national prospective study using highly automated 3-D optical coherence tomography analysis.

Background: There is conflicting evidence on the role of acetylsalicylic acid (ASA) use in the development of cardiac allograft vasculopathy (CAV).

Methods: A nationwide prospective two-center study investigated changes in the coronary artery vasculature by highly automated 3-D optical coherence tomography (OCT) analysis at 1 month and 12 months after heart transplant (HTx). The influence of ASA use on coronary artery microvascular changes was analyzed in the overall study cohort and after propensity score matching for selected clinical CAV risk factors.

The accuracy of detailed analysis of optical coherence tomography in detection of plaque lipid content: dual-imaging study with optical coherence tomography and near-infrared spectroscopy.

Background: Lipid-rich plaque covered by a thin fibrous cap (FC) has been identified as a frequent morphological substrate for the development of acute coronary syndrome. Optical coherence tomography (OCT) permits the identification and measurement of the FC. Near-infrared spectroscopy (NIRS) has been approved for detection of coronary lipids.

A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs.

Purpose: The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy.

Methods And Materials: The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment.

Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation.

Rationale And Objectives: Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification problem.

Materials And Methods: T1-CE, T2WI, and FLAIR 3D-segmented masks of 307 patients (157 GB and 150 BM) were generated post resampling, co-registration normalization and semi-automated 3D-segmentation and used for internal model development.

Patch-wise 3D segmentation quality assessment combining reconstruction and regression networks.

Purpose: General deep-learning (DL)-based semantic segmentation methods with expert level accuracy may fail in 3D medical image segmentation due to complex tissue structures, lack of large datasets with ground truth, etc. For expeditious diagnosis, there is a compelling need to predict segmentation quality without ground truth. In some medical imaging applications, maintaining the quality of segmentation is crucial to the localized regions where disease is prevalent rather than just globally maintaining high-average segmentation quality.

Kernel-weighted contribution: a method of visual attribution for 3D deep learning segmentation in medical imaging.

Purpose: Explaining deep learning model decisions, especially those for medical image segmentation, is a critical step toward the understanding and validation that will enable these powerful tools to see more widespread adoption in healthcare. We introduce kernel-weighted contribution, a visual explanation method for three-dimensional medical image segmentation models that produces accurate and interpretable explanations. Unlike previous attribution methods, kernel-weighted contribution is explicitly designed for medical image segmentation models and assesses feature importance using the relative contribution of each considered activation map to the predicted segmentation.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

Purpose: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL).

Methodology: Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR).

Sensitivity of CNN image analysis to multifaceted measurements of neurite growth.

Quantitative analysis of neurite growth and morphology is essential for understanding the determinants of neural development and regeneration, however, it is complicated by the labor-intensive process of measuring diverse parameters of neurite outgrowth. Consequently, automated approaches have been developed to study neurite morphology in a high-throughput and comprehensive manner. These approaches include computer-automated algorithms known as 'convolutional neural networks' (CNNs)-powerful models capable of learning complex tasks without the biases of hand-crafted models.

Segmentation quality assessment by automated detection of erroneous surface regions in medical images.

Despite the advancement in deep learning-based semantic segmentation methods, which have achieved accuracy levels of field experts in many computer vision applications, the same general approaches may frequently fail in 3D medical image segmentation due to complex tissue structures, noisy acquisition, disease-related pathologies, as well as the lack of sufficiently large datasets with associated annotations. For expeditious diagnosis and quantitative image analysis in large-scale clinical trials, there is a compelling need to predict segmentation quality without ground truth. In this paper, we propose a deep learning framework to locate erroneous regions on the boundary surfaces of segmented objects for quality control and assessment of segmentation.

Can Artificial Intelligence Enhance Syncope Management?: A Multidisciplinary Collaborative Statement.

Syncope, a form of transient loss of consciousness, remains a complex medical condition for which adverse cardiovascular outcomes, including death, are of major concern but rarely occur. Current risk stratification algorithms have not completely delineated which patients benefit from hospitalization and specific interventions. Patients are often admitted unnecessarily and at high cost.

Caldendrin represses neurite regeneration and growth in dorsal root ganglion neurons.

Caldendrin is a Ca binding protein that interacts with multiple effectors, such as the Ca1 L-type Ca channel, which play a prominent role in regulating the outgrowth of dendrites and axons (i.e., neurites) during development and in response to injury.

Assisted annotation in Deep LOGISMOS: Simultaneous multi-compartment 3D MRI segmentation of calf muscles.

Background: Automated segmentation of individual calf muscle compartments in 3D MR images is gaining importance in diagnosing muscle disease, monitoring its progression, and prediction of the disease course. Although deep convolutional neural networks have ushered in a revolution in medical image segmentation, achieving clinically acceptable results is a challenging task and the availability of sufficiently large annotated datasets still limits their applicability.

Purpose: In this paper, we present a novel approach combing deep learning and graph optimization in the paradigm of assisted annotation for solving general segmentation problems in 3D, 4D, and generally n-D with limited annotation cost.

Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department.

Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking.

Role of genetics in the development of cardiac allograft vasculopathy.

Background: The association between genetic polymorphisms and early cardiac allograft vasculopathy (CAV) development is relatively unexplored. Identification of genes involved in the CAV process may offer new insights into pathophysiology and lead to a wider range of therapeutic options.

Methods: This prospective study of 109 patients investigated 44 single nucleotide polymorphisms (SNPs) within the susceptibility loci potentially related to coronary artery disease, carotid artery intima-media thickness (cIMT), and in nitric oxide synthase gene.

Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant therapy prior to surgical resection for an optimal treatment outcome.

KCB-Net: A 3D knee cartilage and bone segmentation network via sparse annotation.

Knee cartilage and bone segmentation is critical for physicians to analyze and diagnose articular damage and knee osteoarthritis (OA). Deep learning (DL) methods for medical image segmentation have largely outperformed traditional methods, but they often need large amounts of annotated data for model training, which is very costly and time-consuming for medical experts, especially on 3D images. In this paper, we report a new knee cartilage and bone segmentation framework, KCB-Net, for 3D MR images based on sparse annotation.

Validation of a semi-automatic software for optical coherence tomography - analysis in heart transplanted patients.

Optical Coherence Tomography (OCT) is an intravascular imaging modality enabling detailed evaluation of cardiac allograft vasculopathy (CAV) after heart transplantation (HTx). However, its clinical application remains hampered by time-consuming manual quantitative analysis. We aimed to validate a semi-automated quantitative OCT analysis software (Iowa Coronary Wall Analyzer, ICWA-OCT) to improve OCT-analysis in HTx patients.

CMC-Net: 3D calf muscle compartment segmentation with sparse annotation.

Accurate 3D segmentation of calf muscle compartments in volumetric MR images is essential to diagnose as well as assess progression of muscular diseases. Recently, good segmentation performance was achieved using state-of-the-art deep learning approaches, which, however, require large amounts of annotated data for training. Considering that obtaining sufficiently large medical image annotation datasets is often difficult, time-consuming, and requires expert knowledge, minimizing the necessary sizes of expert-annotated training datasets is of great importance.

Computed Tomography Perfusion-Based Prediction of Core Infarct and Tissue at Risk: Can Artificial Intelligence Help Reduce Radiation Exposure?

Background And Purpose: We explored the feasibility of automated, arterial input function independent, vendor neutral prediction of core infarct, and penumbral tissue using complete and partial computed tomographic perfusion data sets through neural networks.

Methods: Using retrospective computed tomographic perfusion data from 57 patients, split as training/validation (60%/40%), we developed and validated separate 2-dimensional U-net models for cerebral blood flow (CBF) and time to maximum (Tmax) maps calculation to predict core infarct and tissue at risk, respectively. Once trained, the full sets of 28 input images were sequentially reduced to equitemporal 14, 10, and 7 time points.

NeuriteNet: A convolutional neural network for assessing morphological parameters of neurite growth.

Background: During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture.

COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet.

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. By October 2020, more than 44 million people were infected, and more than 1,000,000 deaths were reported. Computed Tomography (CT) images can be used as an alternative to the time-consuming RT-PCR test, to detect COVID-19.