AIMC Topic: Tomography, X-Ray Computed

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A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.

Scientific data
Accurate segmentation of pulmonary structures from computed tomography (CT) is critical for lung disease management, yet progress is hampered by a lack of large-scale, public datasets with comprehensive multi-structure annotations. To address this, w...

CT-based radiomics and deep learning models for predicting thyroid cartilage invasion and patient prognosis in laryngeal carcinoma.

Scientific reports
Accurate assessment of thyroid cartilage invasion is crucial for treatment decision-making and prognosis evaluation in laryngeal squamous cell carcinoma (LSCC). This study aimed to compare the performance of the radiomics and deep learning (DL) model...

Visual language model-assisted spectral CT reconstruction by diffusion and low-rank priors from limited-angle measurements.

Physics in medicine and biology
Spectral computed tomography (CT) is a critical tool in clinical practice, offering capabilities in multi-energy spectrum imaging and material identification. The limited-angle (LA) scanning strategy has attracted attention for its advantages in fast...

Nonenhanced CT-Based radiomics model enhances PTC detection in Hashimoto's thyroiditis.

BMC cancer
BACKGROUND: Hashimoto's thyroiditis (HT) is a common benign thyroid disease that often coexists with papillary thyroid carcinoma (PTC). Owing to the diffuse changes in the thyroid caused by HT, PTCs can be challenging to detect using conventional ima...

Torso synthetic CT generation by integrating deep learning and segmentation for FDG-PET/MR attenuation correction.

Biomedical physics & engineering express
Positron Emission Tomography/Magnetic Resonance () offers benefits over PET/CT including simultaneous PET and MR acquisition, intrinsic spatial registration accuracy, MR-based functional information, and superior soft tissue contrast. However, accura...

CSCST-Net: a fully sparse-regularized convolutional sparse coding network for low-dose CT denoising.

Biomedical physics & engineering express
. Most low-dose computed tomography (LDCT) denoising methods based on CNN have some denoising effect, but their interpretability is very low due to the black-box nature of neural networks.. To address this issue, we propose a novel fully sparse-regul...

CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.

Biomedical engineering online
OBJECTIVES: This study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.

Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.

Scientific reports
Abdominal aortic aneurysm (AAA) progression carries a significant rupture risk, demanding accurate prediction models beyond traditional methods that rely on limited clinical parameters and often overlook complex factor interplay. We aimed to enhance ...

Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins.

Computer assisted surgery (Abingdon, England)
PURPOSE: Couinaud's liver segment classification has been widely adopted for liver surgery planning, yet its rigid anatomical boundaries often fail to align precisely with individual patient anatomy. This study proposes a novel patient-specific liver...

Diagnostic assistance method for RR-TB/MDR-TB patients under treatment based on CNN-LSTM.

Scientific reports
The rapid development of deep learning has promoted its application in disease diagnosis, treatment, and prognosis prediction. Medical imaging plays a crucial role in the management of rifampicin-resistant tuberculosis/multidrug-resistant tuberculosi...