AIMC Topic: Image Processing, Computer-Assisted

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From pixels to insights: Machine learning and deep learning for bioimage analysis.

BioEssays : news and reviews in molecular, cellular and developmental biology
Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, ...

Deep learning-driven multi-view multi-task image quality assessment method for chest CT image.

Biomedical engineering online
BACKGROUND: Chest computed tomography (CT) image quality impacts radiologists' diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This...

Medical image identification methods: A review.

Computers in biology and medicine
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging pr...

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation.

Nature methods
Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convoluti...

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.

Tomography (Ann Arbor, Mich.)
Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy...

Dental bitewing radiographs segmentation using deep learning-based convolutional neural network algorithms.

Oral radiology
OBJECTIVES: Dental radiographs, particularly bitewing radiographs, are widely used in dental diagnosis and treatment Dental image segmentation is difficult for various reasons, such as intricate structures, low contrast, noise, roughness, and unclear...

Image factory: A method for synthesizing novel CT images with anatomical guidance.

Medical physics
BACKGROUND: Deep learning in medical applications is limited due to the low availability of large labeled, annotated, or segmented training datasets. With the insufficient data available for model training comes the inability of these networks to lea...

Dynamic parametric MRI and deep learning: Unveiling renal pathophysiology through accurate kidney size quantification.

NMR in biomedicine
Renal pathologies often manifest as alterations in kidney size, providing a valuable avenue for employing dynamic parametric MRI as a means to derive kidney size measurements for the diagnosis, treatment, and monitoring of renal disease. Furthermore,...

A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis.

Medical physics
BACKGROUND: Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention.