AIMC Topic: Image Processing, Computer-Assisted

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Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: Accurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based i...

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.

Nature communications
As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a ...

Automatic 3D reconstruction of vertebrae from orthogonal bi-planar radiographs.

Scientific reports
When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical ima...

Deep learning for quantifying spatial patterning and formation process of early differentiated human-induced pluripotent stem cells with micropattern images.

Journal of microscopy
Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, w...

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks.

International journal of neural systems
Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the person...

deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks.

Computers in biology and medicine
BACKGROUND: Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in re...

Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.

Topics in magnetic resonance imaging : TMRI
OBJECTIVES: The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for t...

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare.

Scientific reports
AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases...

Virtual tissue microstructure reconstruction across species using generative deep learning.

PloS one
Analyzing tissue microstructure is essential for understanding complex biological systems in different species. Tissue functions largely depend on their intrinsic tissue architecture. Therefore, studying the three-dimensional (3D) microstructure of t...

Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising.

Network (Bristol, England)
Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor c...