Adaptive boundary-enhanced Dice loss for image segmentation.

Deep learning is widely utilized for medical image segmentation, and its effectiveness is significantly influenced by the choice of specialized loss functions. In this study, we introduce an adaptive boundary-enhanced Dice (ABeDice) loss function, which integrates an exponential recursive complementary (ERC) function with the traditional Dice loss to improve segmentation accuracy. The ERC function leverages the prediction probability of each pixel and its complement to enhance the detection and localization of object boundaries.

CT-Based Lung Size Matching in Delayed Chest Closure for Systemic Sclerosis Lung Transplantation.

Purpose: Delayed chest closure (DCC) during lung transplantation (LTx) is a controversial surgical approach that lacks research in systemic sclerosis (SSc) patients. We investigated outcomes, clinical risk factors, and CT-based lung size-matching parameters associated with DCC in SSc recipients.

Methods: This retrospective study included 92 SSc recipients (age 51 years ± 10, 56/92 (61.

CT-Derived Features as Predictors of Clot Burden and Resolution.

: To evaluate the prognostic utility of CT-imaging-derived biomarkers in distinguishing acute pulmonary embolism (PE) resolution and its progression to chronic PE, as well as their association with clot burden. : We utilized a cohort of 45 patients (19 male (42.2%)) and 96 corresponding CT scans with exertional dyspnea following an acute PE.

A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.

Objective: To identify image biomarkers associated with overall life expectancy from low-dose CT and integrate them as an index for assessing an individual's health.

Methods: Two categories of CT image features, body composition tissues and cardiopulmonary vasculature characteristics, were quantified from LDCT scans in the Pittsburgh Lung Screening Study cohort (n = 3635). Cox proportional-hazards models identified significant image features which were integrated with subject demographics to predict the subject's overall hazard.

Body composition as a biomarker for assessing future lung cancer risk.

Purpose: To investigate if body composition is a biomarker for assessing the risk of developing lung cancer.

Materials And Methods: Low-dose computed tomography (LDCT) scans from the Pittsburgh Lung Screening Study (PLuSS) (n=3,635, 22 follow-up years) and NLST-ACRIN (n=16,435, 8 follow-up years) cohorts were used in the study. Artificial intelligence (AI) algorithms were developed to automatically segment and quantify subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone.

Vascular Biomarkers for Pulmonary Nodule Malignancy: Arteries vs. Veins.

Objective: This study aims to investigate the association between the arteries and veins surrounding a pulmonary nodule and its malignancy.

Methods: A dataset of 146 subjects from a LDCT lung cancer screening program was used in this study. AI algorithms were used to automatically segment and quantify nodules and their surrounding macro-vasculature.

Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans.

Objectives: The current understanding of survival prediction of lung transplant (LTx) patients with systemic sclerosis (SSc) is limited. This study aims to identify novel image features from preoperative chest CT scans associated with post-LTx survival in SSc patients and integrate them into comprehensive prediction models.

Materials And Methods: We conducted a retrospective study based on a cohort of SSc patients with demographic information, clinical data, and preoperative chest CT scans who underwent LTx between 2004 and 2020.

Assessing the transportability of radiomic models for lung cancer diagnosis: commercial open-source feature extractors.

Background: Radiomics has shown promise in improving malignancy risk stratification of indeterminate pulmonary nodules (IPNs) with many platforms available, but with no head-to-head comparisons. This study aimed to evaluate transportability of radiomic models across platforms by comparing performances of a commercial radiomic feature extractor (HealthMyne) with an open-source extractor (PyRadiomics) on diagnosis of lung cancer in IPNs.

Methods: A commercial radiomic feature extractor was used to segment IPNs from computed tomography (CT) scans, and a previously validated radiomic model based on commercial features was used as baseline (ComRad).

HRCT findings predict 1-year mortality in patients with acute exacerbation of idiopathic inflammatory myopathies-associated interstitial lung disease.

Background: Acute exacerbation of idiopathic inflammatory myopathies-associated interstitial lung disease (AE-IIM-ILD) is a significant event associated with increased morbidity and mortality. However, few studies investigated the potential prognostic factors contributing to mortality in patients who experience AE-IIM-ILD.

Objectives: The purpose of our study was to comprehensively investigate whether high-resolution computed tomography (HRCT) findings predict the 1-year mortality in patients who experience AE-IIM-ILD.

Pulmonary circulatory system characteristics are associated with future lung cancer risk.

Background: Most of the subjects eligible for annual low-dose computed tomography (LDCT) lung screening will not develop lung cancer for their life. It is important to identify novel biomarkers that can help identify those at risk of developing lung cancer and improve the efficiency of LDCT screening programs.

Objective: This study aims to investigate the association between the morphology of the pulmonary circulatory system (PCS) and lung cancer development using LDCT scans acquired in the screening setting.

XRayWizard: Reconstructing 3-D lung surfaces from a single 2-D chest x-ray image via Vision Transformer.

Background: Chest x-ray is widely utilized for the evaluation of pulmonary conditions due to its technical simplicity, cost-effectiveness, and portability. However, as a two-dimensional (2-D) imaging modality, chest x-ray images depict limited anatomical details and are challenging to interpret.

Purpose: To validate the feasibility of reconstructing three-dimensional (3-D) lungs from a single 2-D chest x-ray image via Vision Transformer (ViT).

Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects.

Purpose: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer.

Methods: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment.

Automated detection and segmentation of pulmonary embolisms on computed tomography pulmonary angiography (CTPA) using deep learning but without manual outlining.

We present a novel computer algorithm to automatically detect and segment pulmonary embolisms (PEs) on computed tomography pulmonary angiography (CTPA). This algorithm is based on deep learning but does not require manual outlines of the PE regions. Given a CTPA scan, both intra- and extra-pulmonary arteries were firstly segmented.

A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer.

The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed.

Batch-balanced focal loss: a hybrid solution to class imbalance in deep learning.

Purpose: To validate the effectiveness of an approach called batch-balanced focal loss (BBFL) in enhancing convolutional neural network (CNN) classification performance on imbalanced datasets.

Materials And Methods: BBFL combines two strategies to tackle class imbalance: (1) batch-balancing to equalize model learning of class samples and (2) focal loss to add hard-sample importance to the learning gradient. BBFL was validated on two imbalanced fundus image datasets: a binary retinal nerve fiber layer defect (RNFLD) dataset () and a multiclass glaucoma dataset ().

Artificial Intelligence Automation of Proptosis Measurement: An Indicator for Pediatric Orbital Abscess Surgery.

Introduction: To evaluate the ability of artificial intelligence (AI) software to quantify proptosis for identifying patients who need surgical drainage.

Methods: We pursued a retrospective study including 56 subjects with a clinical diagnosis of subperiosteal orbital abscess (SPOA) secondary to sinusitis at a tertiary pediatric hospital from 2002 to 2016. AI computer software was developed to perform 3D visualization and quantitative assessment of proptosis from computed tomography (CT) images acquired at the time of hospital admission.

Deep-Masker: A Deep Learning-based Tool to Assess Chord Length from Murine Lung Images.

Chord length is an indirect measure of alveolar size and a critical endpoint in animal models of chronic obstructive pulmonary disease (COPD). In assessing chord length, the lumens of nonalveolar structures are eliminated from measurement by various methods, including manual masking. However, manual masking is resource intensive and can introduce variability and bias.

Computational risk modeling of underground coal mines based on NIOSH employment demographics.

Objective: To investigate the feasibility of predicting the risk of underground coal mine operations using data from the National Institute for Occupational Safety and Health (NIOSH).

Methods: A total of 22,068 data entries from 3,982 unique underground coal mines from 1990 to 2020 were extracted from the NIOSH mine employment database. We defined the risk index of a mine as the ratio between the number of injuries and the size of the mine.

CT-derived body composition associated with lung cancer recurrence after surgery.

Objectives: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence.

Methods: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively.

CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.

Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy.

Methods: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy.

Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).

Objective: To develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification.

Methods: The neural understanding network (NUN) is formed by two components: (1) convolutional neural network (CNN)-based feature extraction and (2) graph neural networks (GNN)-based feature understanding. The CNN-based image features were transformed into a graph representation to encode and visualize long-range feature interactions to identify the image regions that significantly contributed to the classification decision.

A texture-aware U-Net for identifying incomplete blinking from eye videography.

Accurate identification of incomplete blinking from eye videography is critical for the early detection of eye disorders or diseases (e.g., dry eye).

Circulating 1,3-Beta-D-Glucan is Associated with Lung Function, Respiratory Symptoms, and Mediators of Matrix Degradation in Chronic Obstructive Pulmonary Disease.

Introduction: Factors beyond cigarette smoke likely contribute to chronic obstructive pulmonary disease (COPD) pathogenesis. Prior studies demonstrate fungal colonization of the respiratory tract and increased epithelial barrier permeability in COPD. We sought to determine whether 1,3-beta-d-glucan (BDG), a polysaccharide component of the fungal cell wall, is detectable in the plasma of individuals with COPD and associates with clinical outcomes and matrix degradation proteins.

Super U-Net: a modularized generalizable architecture.

Objective: To develop and validate a novel convolutional neural network (CNN) termed "Super U-Net" for medical image segmentation.

Methods: Super U-Net integrates a dynamic receptive field module and a fusion upsampling module into the classical U-Net architecture. The model was developed and tested to segment retinal vessels, gastrointestinal (GI) polyps, skin lesions on several image types (i.