Automatic cardiothoracic ratio calculation based on lung fields abstracted from chest X-ray images without heart segmentation.

Introduction: The cardiothoracic ratio (CTR) based on postero-anterior chest X-rays (P-A CXR) images is one of the most commonly used cardiac measurement methods and an indicator for initially evaluating cardiac diseases. However, the hearts are not readily observable on P-A CXR images compared to the lung fields. Therefore, radiologists often manually determine the CTR's right and left heart border points of the adjacent left and right lung fields to the heart based on P-A CXR images.

Ischemic perfusion radiomics: assessing neurological impairment in acute ischemic stroke.

Introduction: Accurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment.

Hemi-diaphragm detection of chest X-ray images based on convolutional neural network and graphics.

Background: Chest X-rays (CXR) are widely used to facilitate the diagnosis and treatment of critically ill and emergency patients in clinical practice. Accurate hemi-diaphragm detection based on postero-anterior (P-A) CXR images is crucial for the diaphragm function assessment of critically ill and emergency patients to provide precision healthcare for these vulnerable populations.

Objective: Therefore, an effective and accurate hemi-diaphragm detection method for P-A CXR images is urgently developed to assess these vulnerable populations' diaphragm function.

Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network.

Objectives: This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions.

Design: In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated from the time dimension of DSC-PWI images. After selecting whole brain features F from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of F in predicting stroke outcomes.

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection.

Exploring the Effectiveness of Multi-Objective Training for Organ Substructure Segmentation in Medical Imaging.

Chronic obstructive pulmonary disease (COPD) is closely related to the right ventricle and lung lobes. This study focuses on the segmentation of the right ventricle and lung lobes. We conducted experiments using the MMWHS and our lung lobe datasets and evaluated the segmentation using different training models.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier.

A preliminary study of dynamic interactive simulation and computational CT scan of the ideal alveolus model.

Background: While the development of CT imaging technique has brought cognition of in vivo organs, the resolution of CT images and their static characteristics have gradually become barriers of microscopic tissue research.

Purpose: Previous research used the finite element method to study the airflow and gas exchange in the alveolus and acinar to show the fate of inhaled aerosols and studied the diffusive, convective, and sedimentation mechanisms. Our study combines these techniques with CT scan simulation to study the mechanisms of respiratory movement and its imaging appearance.

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution.

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics.

Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD.

Introduction: Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier.

The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction.

Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated.

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.

Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy.

Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke.

Ischemic stroke has become a severe disease endangering human life. However, few studies have analyzed the radiomics features that are of great clinical significance for the diagnosis, treatment, and prognosis of patients with ischemic stroke. Due to sufficient cerebral blood flow information in dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) images, this study aims to find the critical features hidden in DSC-PWI images to characterize hypoperfusion areas (HA) and normal areas (NA).

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network.

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy.

A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke.

Background: The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction.

Methods: The supervised feature selection (Lasso) and unsupervised feature-selection methods (five-feature dimension-reduction algorithms) were used to generate four experimental groups with DRF in different combinations.

Quantitative Distribution of Cerebral Venous Oxygen Saturation and Its Prognostic Value in Patients with Acute Ischemic Stroke.

This study investigated the quantitative distribution of cerebral venous oxygen saturation (SvO2) based on quantitative sensitivity mapping (QSM) and determined its prognostic value in patients with acute ischemic stroke (AIS). A retrospective study was conducted on 39 hospitalized patients. Reconstructed QSM was used to calculate the cerebral SvO2 of each region of interest (ROI) in the ischemic hemisphere.

Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome.

Background: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke.

Methods: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS).

Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier.

Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification.

Early COPD Risk Decision for Adults Aged From 40 to 79 Years Based on Lung Radiomics Features.

Background: Chronic obstructive pulmonary disease (COPD), a preventable lung disease, has the highest prevalence in the elderly and deserves special consideration regarding earlier warnings in this fragile population. The impact of age on COPD is well known, but the COPD risk of the aging process in the lungs remains unclear. Therefore, it is necessary to understand the COPD risk of the aging process in the lungs, providing an early COPD risk decision for adults.

Lung parenchyma parameters measure of rats from pulmonary window computed tomography images based on ResU-Net model for medical respiratory researches.

Our paper proposes a method to measure lung parenchyma parameters from pulmonary window computed tomography images based on ResU-Net model including the CT value, the density, the lung volume, and the surface area of the lungs of healthy rats, to help promote the quantitative analysis of lung parenchyma parameters of rats in medical respiratory researches. Through the analysis of the lung parenchyma parameters of the control group and the treatment group, the law of change among the lung parenchyma parameters is given in our paper. After comparing and analyzing the lung parenchyma parameter CT value and the density of the two groups, it is discovered that the lung parenchyma parameter CT value and the density significantly increase in the treatment group which is after continuously inhaling the nebulization of contrast agents.