High-resolution and high-fidelity diffusion tensor imaging of cervical spinal cord using 3D reduced-FOV multiplexed sensitivity encoding (3D-rFOV-MUSE).

Purpose: To develop a 3D isotropic high-resolution and high-fidelity cervical spinal cord DTI technique for addressing the current challenges existing in 2D cervical spinal cord DTI.

Methods: A 3D multi-shot DWI acquisition and reconstruction technique was developed by combining 3D multiplexed sensitivity encoding (3D-MUSE) with two reduced FOV techniques, termed 3D-rFOV-MUSE, to acquire 3D cervical spinal cord DTI data using a sagittal thin slab. A self-referenced 2D ghost correction method and a 2D navigator-based inter-shot phase correction were integrated into the reconstruction framework to simultaneously eliminate Nyquist ghost and aliasing artifacts.

CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: A multicentre cohort study.

Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in patients with CD and compared it with a subcutaneous adipose tissue (SAT)-RM.

Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning.

Purpose: Using deep learning (DL)-based technique, we identify risk factors and create a prediction model for refractory neovascular age-related macular degeneration (nAMD) characterized by persistent disease activity (PDA) in spectral domain optical coherence tomography (SD-OCT) images.

Materials And Methods: A total of 671 typical B-scans were collected from 186 eyes of 186 patients with nAMD. Spectral domain optical coherence tomography images were analyzed using a classification convolutional neural network (CNN) and a fully convolutional network (FCN) algorithm to extract six features involved in nAMD, including ellipsoid zone (EZ), external limiting membrane (ELM), intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelium detachment (PED), and subretinal hyperreflective material (SHRM).

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

Objectives: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently.

Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient Approach to Multi-Modality Medical Image Segmentation.

Due to the lack of properly annotated medical data, exploring the generalization capability of the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in recent years to equip the deep model with the ability to recognize unseen classes. However, existing studies mainly focus on natural images, which utilize linguistic models to extract auxiliary information for ZSL.

Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms.

Current clinical practice or radiomics studies of pancreatic neuroendocrine neoplasms (pNENs) require manual delineation of the lesions in computed tomography (CT) images, which is time-consuming and subjective. We used a semi-automatic deep learning (DL) method for segmentation of pNENs and verified its feasibility in radiomics analysis. This retrospective study included two datasets: Dataset 1, contrast-enhanced CT images (CECT) of 80 and 18 patients respectively collected from two centers; and Dataset 2, CECT of 56 and 16 patients respectively from two centers.

Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn's Disease.

Background & Aims: No reliable method for evaluating intestinal fibrosis in Crohn's disease (CD) exists; therefore, we developed a computed-tomography enterography (CTE)-based radiomic model (RM) for characterizing intestinal fibrosis in CD.

Methods: This retrospective multicenter study included 167 CD patients with 212 bowel lesions (training, 98 lesions; test, 114 lesions) who underwent preoperative CTE and bowel resection at 1 of the 3 tertiary referral centers from January 2014 through June 2020. Bowel fibrosis was histologically classified as none-mild or moderate-severe.

AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography.

Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging.

Uncertainty-aware domain alignment for anatomical structure segmentation.

Automatic and accurate segmentation of anatomical structures on medical images is crucial for detecting various potential diseases. However, the segmentation performance of established deep neural networks may degenerate on different modalities or devices owing to the significant difference across the domains, a problem known as domain shift. In this work, we propose an uncertainty-aware domain alignment framework to address the domain shift problem in the cross-domain Unsupervised Domain Adaptation (UDA) task.

Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network.

Objective: Deep vein thrombosis (DVT) is a disease caused by abnormal blood clots in deep veins. Accurate segmentation of DVT is important to facilitate the diagnosis and treatment. In the current study, we proposed a fully automatic method of DVT delineation based on deep learning (DL) and contrast enhanced magnetic resonance imaging (CE-MRI) images.