AIMC Topic: Image Processing, Computer-Assisted

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Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.

Medical physics
PURPOSE: Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other ...

Medical image classification using synergic deep learning.

Medical image analysis
The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains c...

Training improvements for ultrasound beamforming with deep neural networks.

Physics in medicine and biology
This paper investigates practical considerations of training ultrasound deep neural network (DNN) beamformers. First, we studied training DNNs using the combination of multiple point target responses instead of single point target responses. Next, we...

Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks.

BMC bioinformatics
BACKGROUND: Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be d...

End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning.

IEEE transactions on pattern analysis and machine intelligence
We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as input and produces the corresponding camera control signals as output (e.g., move forward, turn left, etc.). Conventional methods tackle tracking an...

Generative Adversarial Networks for Facilitating Stain-Independent Supervised and Unsupervised Segmentation: A Study on Kidney Histology.

IEEE transactions on medical imaging
A major challenge in the field of segmentation in digital pathology is given by the high effort for manual data annotations in combination with many sources introducing variability in the image domain. This requires methods that are able to cope with...

SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network.

Zeitschrift fur medizinische Physik
Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonanc...

Simulation-based deep artifact correction with Convolutional Neural Networks for limited angle artifacts.

Zeitschrift fur medizinische Physik
Non-conventional scan trajectories for interventional three-dimensional imaging promise low-dose interventions and a better radiation protection to the personnel. Circular tomosynthesis (cTS) scan trajectories yield an anisotropical image quality dis...

Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.

Medical physics
PURPOSE: In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in th...

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.

IEEE transactions on medical imaging
Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniq...