AIMC Topic: Image Processing, Computer-Assisted

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Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assi...

A review of robot-assisted ultrasound examination: Systems and technology.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: At present, the number and overall level of ultrasound (US) doctors cannot meet the medical needs, and the medical ultrasound robots will largely solve the shortage of medical resources.

[Deep Learning-Based Artificial Intelligence Model for Automatic Carotid Plaque Identification].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
This study aims at developing a dataset for determining the presence of carotid artery plaques in ultrasound images, composed of 1761 ultrasound images from 1165 participants. A deep learning architecture that combines bilinear convolutional neural n...

Improved optimizer with deep learning model for emotion detection and classification.

Mathematical biosciences and engineering : MBE
Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Non...

[Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18].

Zhonghua yi xue za zhi
To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. A cross-sectional study. Twenty-five narcolepsy type 1 patients who wer...

Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the firs...

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.

Cancer research
Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which...

Structure Preserving Cycle-Gan for Unsupervised Medical Image Domain Adaptation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. This work introduces the Structure Preserving Cycle-GAN (SP Cycle-GAN) for unsu...

Classification of Carotid Plaque with Jellyfish Sign Through Convolutional and Recurrent Neural Networks Utilizing Plaque Surface Edges.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In carotid arteries, plaque can develop as localized elevated lesions. The Jellyfish sign, marked by fluctuating plaque surfaces with blood flow pulsation, is a dynamic characteristic of these plaques that has recently attracted attention. Detecting ...

Residual Neural Networks for the Prediction of the Regularization Parameters in PET Reconstruction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Positron Emission Tomography (PET) is a medical imaging modality relying on numerical methods that integrate the statistical properties of the measurements and prior assumptions about the images. In order to maximize the computed image quality, PET r...