AIMC Topic: Tomography, X-Ray Computed
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SynMSE: A multimodal similarity evaluator for complex distribution discrepancy in unsupervised deformable multimodal medical image registration.
Medical image analysis
Apr 22, 2025
Unsupervised deformable multimodal medical image registration often confronts complex scenarios, which include intermodality domain gaps, multi-organ anatomical heterogeneity, and physiological motion variability. These factors introduce substantial ...
Improved radiological diagnosis of osteoporotic vertebral fragility fractures following UK-wide interventions and re-audit-can this be maintained and translated into clinical practice?
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
Apr 22, 2025
UNLABELLED: To determine the potential economic, morbidity and mortality impact of improvements in reporting of vertebral fragility fractures (VFFs) following a complete audit cycle. Six percent interval increase in reporting of moderate/severe VFFs ...
Deep learning-aided segmentation combined with finite element analysis reveals a more natural biomechanic of dinosaur fossil.
Scientific reports
Apr 22, 2025
Finite element analysis (FEA), a biomechanical simulation technique capable of providing direct mechanical visualization for CT-based digital models, has been extensively applied to fossil image datasets to address key evolutionary questions in paleo...
CAD-Unet: A capsule network-enhanced Unet architecture for accurate segmentation of COVID-19 lung infections from CT images.
Medical image analysis
Apr 19, 2025
Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accu...
Habitat Radiomics and Deep Learning Features Based on CT for Predicting Lymphovascular Invasion in T1-stage Lung Adenocarcinoma: A Multicenter Study.
Academic radiology
Apr 18, 2025
RATIONALE AND OBJECTIVES: The research aims to examine how CT-derived habitat radiomics can be used to predict lymphovascular invasion (LVI) in patients with T1-stage lung adenocarcinoma (LUAD), and compare its effectiveness to traditional radiomics ...
Fully Automated Online Adaptive Radiation Therapy Decision-Making for Cervical Cancer Using Artificial Intelligence.
International journal of radiation oncology, biology, physics
Apr 17, 2025
BACKGROUND: Interfraction variations during radiation therapy pose a challenge for patients with cervical cancer, highlighting the benefits of online adaptive radiation therapy (oART). However, adaptation decisions rely on subjective image reviews by...
Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.
BMC musculoskeletal disorders
Apr 17, 2025
RATIONALE AND OBJECTIVES: To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep learning-based automatic segmentation of the proximal femur with a radiomics-based bone status classifi...
Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review.
Medicina (Kaunas, Lithuania)
Apr 16, 2025
: This scoping review explores the current state of the art of AI-based applications in the field of orthopedics, focusing on its implementation in diagnostic imaging and preoperative planning of knee joint procedures. : The search was carried out us...
Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation.
Scientific reports
Apr 16, 2025
Segmenting the spine from CT images is crucial for diagnosing and treating spine-related conditions but remains challenging due to the spine's complex anatomy and imaging artifacts. This study introduces a novel encoder-decoder-based deep learning ap...
A deep learning approach for quantifying CT perfusion parameters in stroke.
Biomedical physics & engineering express
Apr 16, 2025
. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residu...
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