AIMC Topic: Imaging, Three-Dimensional
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BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.
Computer methods and programs in biomedicine
Jan 10, 2025
BACKGROUND AND OBJECTIVE: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture lo...
Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.
Scientific reports
Jan 10, 2025
Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate compu...
G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.
International journal of computer assisted radiology and surgery
Jan 9, 2025
PURPOSE: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settin...
Feasibility of occlusal plane in predicting the changes in anteroposterior mandibular position: a comprehensive analysis using deep learning-based three-dimensional models.
BMC oral health
Jan 8, 2025
BACKGROUND: A comprehensive analysis of the occlusal plane (OP) inclination in predicting anteroposterior mandibular position (APMP) changes is still lacking. This study aimed to analyse the relationships between inclinations of different OPs and APM...
Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.
AJNR. American journal of neuroradiology
Jan 8, 2025
BACKGROUND AND PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) is reversible dementia that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1...
DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning-Based Image Quality Improvement for Vessel Wall MRI.
AJNR. American journal of neuroradiology
Jan 8, 2025
BACKGROUND AND PURPOSE: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied t...
Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.
International journal of computer assisted radiology and surgery
Jan 7, 2025
PURPOSE: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We pr...
Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning.
Scientific reports
Jan 7, 2025
BACKGROUND AND OBJECTIVES: For the planning of surgical procedures involving the bony reconstruction of the mandible, the autologous iliac crest graft, along with the fibula graft, has become established as a preferred donor region. While computer-as...
CLP-Net: an advanced artificial intelligence technique for localizing standard planes of cleft lip and palate by three-dimensional ultrasound in the first trimester.
BMC pregnancy and childbirth
Jan 7, 2025
BACKGROUND: Early diagnosis of cleft lip and palate (CLP) requires a multiplane examination, demanding high technical proficiency from radiologists. Therefore, this study aims to develop and validate the first artificial intelligence (AI)-based model...
Attention-Guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic ...
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