AIMC Topic: Image Processing, Computer-Assisted

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Femoral image segmentation based on two-stage convolutional network using 3D-DMFNet and 3D-ResUnet.

Computer methods and programs in biomedicine
OBJECTIVE: The femur is a typical human long bone with an irregular spatial structure. Femoral fractures are the most common occurrence in middle-aged and older adults. The structure of human bone tissue is very complex, and there are significant dif...

Anticipation for surgical workflow through instrument interaction and recognized Signals.

Medical image analysis
Surgical workflow anticipation is an essential task for computer-assisted intervention (CAI) systems. It aims at predicting the future surgical phase and instrument occurrence, providing support for intra-operative decision-support system. Recent stu...

AFINet: Attentive Feature Integration Networks for image classification.

Neural networks : the official journal of the International Neural Network Society
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Residual-like networks, such as ResNets, mainly focus on the skip connection to avoid gradient vanishing. However, the...

Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism.

Computational intelligence and neuroscience
Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with comp...

Visual Detection and Image Processing of Parking Space Based on Deep Learning.

Sensors (Basel, Switzerland)
The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking spac...

Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning.

Sensors (Basel, Switzerland)
Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have s...

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.

Zeitschrift fur medizinische Physik
INTRODUCTION: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brains containing significant susceptibility sources, such as intracranial...

Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep learning-based methods for fast target segmentation of magnetic resonance imaging (MRI) have become increasingly popular in recent years. Generally, the success of deep learning methods in medical image segmentation tas...

Automated Calculation of Liquid Crystal Sensing Images Based on Deep Learning.

Analytical chemistry
Liquid crystal (LC)-based sensors have been extensively applied in the detection of chemical and biological events. However, the calculation of the optical images of the LC-based sensors is usually time-consuming and also might bring some errors due ...

Shape-Texture Debiased Training for Robust Template Matching.

Sensors (Basel, Switzerland)
Finding a template in a search image is an important task underlying many computer vision applications. This is typically solved by calculating a similarity map using features extracted from the separate images. Recent approaches perform template mat...