AIMC Topic: Image Processing, Computer-Assisted

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Benchmarking of Deep Architectures for Segmentation of Medical Images.

IEEE transactions on medical imaging
In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using ...

Deep-Learning-Based Electrical Noise Removal Enables High Spectral Optoacoustic Contrast in Deep Tissue.

IEEE transactions on medical imaging
Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Previously employed signal processing techniques have proven insufficient to remove the ...

Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning.

IEEE transactions on medical imaging
Emerging deep learning-based methods have enabled great progress in automatic neuron segmentation from Electron Microscopy (EM) volumes. However, the success of existing methods is heavily reliant upon a large number of annotations that are often exp...

Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation.

IEEE transactions on neural networks and learning systems
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised s...

FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring.

Sensors (Basel, Switzerland)
In satellite remote sensing applications, waterbody segmentation plays an essential role in mapping and monitoring the dynamics of surface water. Satellite image segmentation-examining a relevant sensor data spectrum and identifying the regions of in...

AI-Generated Face Image Identification with Different Color Space Channel Combinations.

Sensors (Basel, Switzerland)
With the rapid development of the Internet and information technology (in particular, generative adversarial networks and deep learning), network data are exploding. Due to the misuse of technology and inadequate supervision, deep-network-generated f...

Fast Deformable Image Registration for Real-Time Target Tracking During Radiation Therapy Using Cine MRI and Deep Learning.

International journal of radiation oncology, biology, physics
PURPOSE: We developed a deep learning (DL) model for fast deformable image registration using 2-dimensional sagittal cine magnetic resonance imaging (MRI) acquired during radiation therapy and evaluated its potential for real-time target tracking com...

Stain-Independent Deep Learning-Based Analysis of Digital Kidney Histopathology.

The American journal of pathology
Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain...

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.

IEEE transactions on visualization and computer graphics
We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-field microscopy. NeuroConstruct offers a Segmentation Toolbox with various annotation helper functio...