AIMC Topic: Tomography, X-Ray Computed

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U-Attention-Net: a deep learning automatic delineation model for parotid glands in head and neck cancer organs at risk on radiotherapy localization computed tomography images.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
OBJECTIVE: This study aimed to develop a novel deep learning model, U-Attention-Net (UA-Net), for precise segmentation of parotid glands on radiotherapy localization CT images.

Multimodal AI framework for lung cancer diagnosis: Integrating CNN and ANN models for imaging and clinical data analysis.

Computers in biology and medicine
Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the critical need for accurate and early diagnostic solutions. This study introduces a novel multimodal artificial intelligence (AI) framework that integrates Conv...

Automatic adult age estimation using bone mineral density of proximal femur via deep learning.

Forensic science international
Accurate adult age estimation (AAE) is critical for forensic and anthropological applications, yet traditional methods relying on bone mineral density (BMD) face significant challenges due to biological variability and methodological limitations. Thi...

Large medical image database impact on generalizability of synthetic CT scan generation.

Computers in biology and medicine
This study systematically examines the impact of training database size and the generalizability of deep learning models for synthetic medical image generation. Specifically, we employ a Cycle-Consistency Generative Adversarial Network (CycleGAN) wit...

Pancreas segmentation in CT scans: A novel MOMUNet based workflow.

Computers in biology and medicine
Automatic pancreas segmentation in CT scans is crucial for various medical applications, including early diagnosis and computer-assisted surgery. However, existing segmentation methods remain suboptimal due to significant pancreas size variations acr...

Trustworthy AI for stage IV non-small cell lung cancer: Automatic segmentation and uncertainty quantification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of lung tumors is essential for advancing personalized medicine in non-small cell lung cancer (NSCLC). However, stage IV NSCLC presents significant challenges due to heterogeneous tumor morphology and the presence of associated ...

CALIMAR-GAN: An unpaired mask-guided attention network for metal artifact reduction in CT scans.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
High-quality computed tomography (CT) scans are essential for accurate diagnostic and therapeutic decisions, but the presence of metal objects within the body can produce distortions that lower image quality. Deep learning (DL) approaches using image...

Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis.

Clinical imaging
BACKGROUND AND AIMS: Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However, OS heterogeneity results in variable treatment responses, highlighting the need for reliable, non-...

A diffusion-stimulated CT-US registration model with self-supervised learning and synthetic-to-real domain adaptation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In abdominal interventional procedures, achieving precise registration of 2D ultrasound (US) frames with 3D computed tomography (CT) scans presents a significant challenge. Traditional tracking methods often rely on high-precision sensors, which can ...