OBJECTIVE: Robotic-guided interventions are emerging techniques that are gradually becoming a common tool for performing biopsies and tumor ablations in liver. This systematic review aims to evaluate their advancements, challenges, and outcomes.
OBJECTIVE: To develop and evaluate a fully automated method based on deep learning and phantomless internal calibration for bone mineral density (BMD) measurement and opportunistic low BMD (osteopenia and osteoporosis) screening using chest low-dose ...
Cancer imaging : the official publication of the International Cancer Imaging Society
Oct 22, 2023
OBJECTIVES: This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.
OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.
Techniques in vascular and interventional radiology
Oct 20, 2023
The accuracy of the robotic device not only relies on a reproducible needle advancement, but also on the possibility to correct target movement at chosen checkpoints and to deviate from a linear to a nonlinear trajectory. We report our experience in ...
When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a dee...
PURPOSE: To compare image quality of contrast-enhanced abdominal-CT using 1st-generation Dual Source Photon-Counting Detector CT (DS-PCD-CT) versus 2nd-generation Dual-Source Energy Integrating-Detector CT (DS-EID-CT) in patients with BMI ≥ 25, apply...
Organ segmentation in abdominal or thoracic computed tomography (CT) images plays a crucial role in medical diagnosis as it enables doctors to locate and evaluate organ abnormalities quickly, thereby guiding surgical planning, and aiding treatment de...
This study aimed at developing a fully automatic technique for right lobe graft weight estimation using deep learning algorithms. The proposed method consists of segmentation of the full liver region from computed tomography (CT) images, classificati...
To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following...
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