AIMC Topic: Imaging, Three-Dimensional

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A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.

Scientific reports
Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, both in terms of prevention and treatment, it is very beneficial to patients. Therefore, our team prop...

Ensemble methods and partially-supervised learning for accurate and robust automatic murine organ segmentation.

Scientific reports
Delineation of multiple organs in murine µCT images is crucial for preclinical studies but requires manual volumetric segmentation, a tedious and time-consuming process prone to inter-observer variability. Automatic deep learning-based segmentation c...

Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network.

Scientific reports
This study investigates a deep learning approach for sex estimation using 3D hyoid bone models derived from computed tomography (CT) scans of a Croatian population. We analyzed 202 hyoid samples (101 male, 101 female), converting CT-derived meshes in...

Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes.

Scientific reports
This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical outcomes in patients suffering from fibromyalgia syndrome (FMS). A cohort of 303 FMS patients, diagn...

Super-resolution of 3D medical images by generative adversarial networks with long and short-term memory and attention.

Scientific reports
Since 3D medical imaging data is a string of sequential images, there is a strong correlation between consecutive images. Deep convolutional networks perform well in extracting spatial features, but are less capable for processing sequence data compa...

Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features.

Scientific reports
Classifying chondroid tumors is an essential step for effective treatment planning. Recently, with the advances in computer-aided diagnosis and the increasing availability of medical imaging data, automated tumor classification using deep learning sh...

Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments.

eNeuro
Marker-based motion capture (MBMC) is a powerful tool for precise, high-speed, three-dimensional tracking of animal movements, enabling detailed study of behaviors ranging from subtle limb trajectories to broad spatial exploration. Despite its proven...

CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy.

eLife
Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised lear...

MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning.

PloS one
Cervical cancer is one of the most aggressive malignant tumours of the reproductive system, posing a significant global threat to women's health. Accurately segmenting cervical tumours in MR images remains a challenging task due to the complex charac...

High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.

Physics in medicine and biology
To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung ...