AIMC Topic: Image Processing, Computer-Assisted

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Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning.

Computational intelligence and neuroscience
In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, rea...

Deep vessel segmentation by learning graphical connectivity.

Medical image analysis
We propose a novel deep learning based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without consideration of the graphical structure of vessel shape. Effective ...

A modality conversion approach to MV-DRs and KV-DRRs registration using information bottlenecked conditional generative adversarial network.

Medical physics
PURPOSE: As affordable equipment, electronic portal imaging devices (EPIDs) are wildly used in radiation therapy departments to verify patients' positions for accurate radiotherapy. However, these devices tend to produce visually ambiguous and low-co...

Effect of Lower Third Molar Segmentations on Automated Tooth Development Staging using a Convolutional Neural Network.

Journal of forensic sciences
Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Dee...

Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel...

Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification.

Journal of neuroscience methods
BACKGROUND: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often bottlenecked by manual analysis...

Automatic segmentation of the uterus on MRI using a convolutional neural network.

Computers in biology and medicine
BACKGROUND: This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders.

A deep learning method for image-based subject-specific local SAR assessment.

Magnetic resonance in medicine
PURPOSE: Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to inclu...

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.

Neural networks : the official journal of the International Neural Network Society
In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimo...

Gabor wavelet-based deep learning for skin lesion classification.

Computers in biology and medicine
Skin cancer cases are increasing and becoming one of the main problems worldwide. Skin cancer is known as a malignant type of skin lesion, and early detection and treatment are necessary. Malignant melanoma and seborrheic keratosis are known as commo...