AIMC Topic: Tomography, X-Ray Computed

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Metaheuristic-optimized generative adversarial network for enhanced sparse-view low-dose CT reconstruction.

Biomedical physics & engineering express
Sparse-view low-dose computed tomography (LDCT) imaging poses difficulties in preserving image quality while reducing radiation exposure. Recent research has focused extensively on artificial intelligence (AI) to reduce artifacts in LDCT. This paper ...

Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.

BMC oral health
BACKGROUND: Accurate landmark localization is important for three-dimensional (3D) cephalometric analysis. Although deep learning has shown promising performance for 3D landmark localization, the high computational burden of processing volumetric dat...

Geometric Fidelity of Magnetic Resonance Imaging and Computed Tomography-Derived Virtual 3D Models of Porcine Cadaver Mandibles: Conventional Versus Artificial Intelligence-Based Segmentation.

Oral health & preventive dentistry
PURPOSE: The workflow for virtual surgical planning (VSP) and the application of CAD/CAM (computer-aided design/computer-aided manufacturing) procedures are mainly based on computed tomography (CT) derived DICOM data sets. Alternatively, this study a...

Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning.

BMC medical imaging
BACKGROUND: Radiomics and artificial intelligence have shown strong predictive capabilities in urinary stone research, particularly concerning stone composition, characteristics, and treatment outcomes. However, the association of stone radiomics and...

Deep learning for automatic segmentation of hepatocellular carcinoma in contrast enhanced CT scans.

Scientific reports
Liver cancer represents a significant cause of cancer-related mortality, with hepatocellular carcinoma (HCC) being the most prevalent forms. Computed tomography (CT) serves as the principal imaging modality for the diagnosis of liver tumors, particul...

SVNC-Net: An optimized U-Net variant with 2D convolutions for lightweight 3D spleen segmentation.

PloS one
Accurate measurement of spleen volume is essential for the diagnosis of splenomegaly. While Computed Tomography (CT) is among the most reliable imaging modalities for this task, manual segmentation of the spleen is labor-intensive and impractical for...

Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC.

Scientific reports
ccRCC is an aggressive, heterogeneous tumor with a poor prognosis. Prognostic assessments need multi-modal data. Radiological images have limits, while pathological images offer micro-level details. Integrating these for ccRCC outcome prediction is i...

Hybrid intelligence in medical image segmentation.

Scientific reports
Medical image segmentation is vital for precise identification and analysis of anatomical structures and pathological regions, yet traditional models often fall short in aligning with clinical workflows, requiring extensive manual correction even whe...

Development and validation of the risk stratification based on deep learning and radiomics to predict survival of advanced cervical cancer.

Scientific reports
Advanced cervical cancer (aCC) is associated with a poor prognosis. This study aimed to develop and validate a deep learning-based risk stratification model to predict overall survival in aCC patients using pre-treatment CT images. A total of 396 pat...

Global trends and hotspots in AI applications for CT detection of chronic obstructive pulmonary disease: A bibliometric analysis from 2012 to 2024.

Lasers in medical science
PURPOSE: Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory lung disease that significantly impacts global health. This study aims to comprehensively analyze global trends and research hotspots in the application of artificial...