Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI.

Glioma is the most common type of primary malignant brain tumor. Accurate survival time prediction for glioma patients may positively impact treatment planning. In this paper, we develop an automatic survival time prediction tool for glioblastoma patients along with an effective solution to the limited availability of annotated medical imaging datasets.

Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification.

A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources.

Deep Feature Stability Analysis Using CT Images of a Physical Phantom Across Scanner Manufacturers, Cartridges, Pixel Sizes, and Slice Thickness.

Image acquisition parameters for computed tomography scans such as slice thickness and field of view may vary depending on tumor size and site. Recent studies have shown that some radiomics features were dependent on voxel size (= pixel size × slice thickness), and with proper normalization, this voxel size dependency could be reduced. Deep features from a convolutional neural network (CNN) have shown great promise in characterizing cancers.

Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's.

Noninvasive diagnosis of lung cancer in early stages is one task where radiomics helps. Clinical practice shows that the size of a nodule has high predictive power for malignancy. In the literature, convolutional neural networks (CNNs) have become widely used in medical image analysis.

Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data.

: Due to the high incidence and mortality rates of lung cancer worldwide, early detection of a precancerous lesion is essential. Low-dose computed tomography is a commonly used technique for screening, diagnosis, and prognosis of non-small-cell lung cancer. Recently, convolutional neural networks (CNN) had shown great potential in lung nodule classification.

Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features.

Quantitative features are generated from a tumor phenotype by various data characterization, feature-extraction approaches and have been used successfully as a biomarker. These features give us information about a nodule, for example, nodule size, pixel intensity, histogram-based information, and texture information from wavelets or a convolution kernel. Semantic features, on the other hand, can be generated by an experienced radiologist and consist of the common characteristics of a tumor, for example, location of a tumor, fissure, or pleural wall attachment, presence of fibrosis or emphysema, concave cut on nodule surface.

Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial.

Background: Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow-up screens and performed size-specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6-16 mm [intermediate], and ≥16 mm [large]).

Methods: We extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow-up (T1).

Predicting malignant nodules by fusing deep features with classical radiomics features.

Lung cancer has a high incidence and mortality rate. Early detection and diagnosis of lung cancers is best achieved with low-dose computed tomography (CT). Classical radiomics features extracted from lung CT images have been shown as able to predict cancer incidence and prognosis.

Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern.

Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction.

Purpose: Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults. Most GBMs exhibit extensive regional heterogeneity at tissue, cellular, and molecular scales, but the clinical relevance of the observed spatial imaging characteristics remains unknown. We investigated pretreatment magnetic resonance imaging (MRI) scans of GBMs to identify tumor subregions and quantify their image-based spatial characteristics that are associated with survival time.

Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis.

Purpose: To evaluate heterogeneity within tumor subregions or "habitats" via textural kinetic analysis on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the classification of two clinical prognostic features; 1) estrogen receptor (ER)-positive from ER-negative tumors, and 2) tumors with four or more viable lymph node metastases after neoadjuvant chemotherapy from tumors without nodal metastases.

Materials And Methods: Two separate volumetric DCE-MRI datasets were obtained at 1.5T, comprised of bilateral axial dynamic 3D T1 -weighted fat suppressed gradient recalled echo-pulse sequences obtained before and after gadolinium-based contrast administration.

Test-retest reproducibility analysis of lung CT image features.

Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility.

Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons.

Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons.

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods.

Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach.

A single click ensemble segmentation (SCES) approach based on an existing "Click&Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases.

A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

Purpose: To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).

Materials And Methods: For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared.

Radiomics: the process and the challenges.

"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures.

A Scalable Framework For Cluster Ensembles.

An ensemble of clustering solutions or partitions may be generated for a number of reasons. If the data set is very large, clustering may be done on tractable size disjoint subsets. The data may be distributed at different sites for which a distributed clustering solution with a final merging of partitions is a natural fit.

A Scalable Framework For Segmenting Magnetic Resonance Images.

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets.

Fast support vector machines for continuous data.

Support vector machines (SVMs) can be trained to be very accurate classifiers and have been used in many applications. However, the training time and, to a lesser extent, prediction time of SVMs on very large data sets can be very long. This paper presents a fast compression method to scale up SVMs to large data sets.

A methodology for extracting objective color from images.

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges.

A modeling approach for burn scar assessment using natural features and elastic property.

A modeling approach is presented for quantitative burn scar assessment. Emphases are given to: 1) constructing a finite-element model from natural image features with an adaptive mesh and 2) quantifying the Young's modulus of scars using the finite-element model and regularization method. A set of natural point features is extracted from the images of burn patients.

Recognizing plankton images from the shadow image particle profiling evaluation recorder.

We present a system to recognize underwater plankton images from the shadow image particle profiling evaluation recorder (SIPPER). The challenge of the SIPPER image set is that many images do not have clear contours. To address that, shape features that do not heavily depend on contour information were developed.

Selection of patients for clinical trials: an interactive web-based system.

The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual procedure.