AIMC Topic: Image Processing, Computer-Assisted

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An Improved Pulse-Coupled Neural Network Model for Pansharpening.

Sensors (Basel, Switzerland)
Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fid...

Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging.

European journal of nuclear medicine and molecular imaging
PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to impr...

Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization.

NeuroImage
The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a w...

Deep learning-based fully automatic segmentation of wrist cartilage in MR images.

NMR in biomedicine
The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensu...

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review).

International journal of oncology
The new era of artificial intelligence (AI) has introduced revolutionary data‑driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision‑support systems. These advances...

Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.

Radiation oncology (London, England)
BACKGROUND: Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent ...

Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis.

Physics in medicine and biology
Deep convolutional neural network (DCNN), now popularly called artificial intelligence (AI), has shown the potential to improve over previous computer-assisted tools in medical imaging developed in the past decades. A DCNN has millions of free parame...

Denoising of multi b-value diffusion-weighted MR images using deep image prior.

Physics in medicine and biology
The clinical value of multiple b-value diffusion-weighted (DW) magnetic resonance imaging (MRI) has been shown in many studies. However, DW-MRI often suffers from low signal-to-noise ratio, especially at high b-values. To address this limitation, we ...

A Strictly Unsupervised Deep Learning Method for HEp-2 Cell Image Classification.

Sensors (Basel, Switzerland)
Classifying the images that portray the Human Epithelial cells of type 2 (HEp-2) represents one of the most important steps in the diagnosis procedure of autoimmune diseases. Performing this classification manually represents an extremely complicated...