AIMC Topic: Image Processing, Computer-Assisted

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Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time imp...

Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy.

Urology
OBJECTIVE: To develop a convolutional neural network to recognize the seminal vesicle and vas deferens (SV-VD) in the posterior approach of robot-assisted radical prostatectomy (RARP) and assess the performance of the convolutional neural network mod...

Automated quantification and statistical assessment of proliferating cardiomyocyte rates in embryonic hearts.

American journal of physiology. Heart and circulatory physiology
The use of digital image analysis and count regression models contributes to the reproducibility and rigor of histological studies in cardiovascular research. The use of formalized computer-based quantification strategies of histological images essen...

Context encoder transfer learning approaches for retinal image analysis.

Computers in biology and medicine
During the last years, deep learning techniques have emerged as powerful alternatives to solve biomedical image analysis problems. However, the training of deep neural networks usually needs great amounts of labeled data to be done effectively. This ...

Self-mentoring: A new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation.

Computers in biology and medicine
BACKGROUND: Accurate segmentation of microscopic structures such as bio-artificial capsules in microscopy imaging is a prerequisite to the computer-aided understanding of important biomechanical phenomenons. State-of-the-art segmentation performances...

CAN: Context-assisted full Attention Network for brain tissue segmentation.

Medical image analysis
Brain tissue segmentation is of great value in diagnosing brain disorders. Three-dimensional (3D) and two-dimensional (2D) segmentation methods for brain Magnetic Resonance Imaging (MRI) suffer from high time complexity and low segmentation accuracy,...

Deep Learning for Breast MRI Style Transfer with Limited Training Data.

Journal of digital imaging
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of sim...

A dense residual U-net for multiple sclerosis lesions segmentation from multi-sequence 3D MR images.

International journal of medical informatics
Multiple Sclerosis (MS) is an autoimmune disease that causes brain and spinal cord lesions, which magnetic resonance imaging (MRI) can detect and characterize. Recently, deep learning methods have achieved remarkable results in the automated segmenta...

RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation.

Sensors (Basel, Switzerland)
Automatic crack detection is always a challenging task due to the inherent complex backgrounds, uneven illumination, irregular patterns, and various types of noise interference. In this paper, we proposed a U-shaped encoder-decoder semantic segmentat...