AIMC Topic: Image Processing, Computer-Assisted

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Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks.

BMC bioinformatics
BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells an...

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.

eLife
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective...

A Novel Automatic Morphologic Analysis of Eyelids Based on Deep Learning Methods.

Current eye research
: To propose a deep-learning-based approach to automatically and objectively evaluate morphologic eyelid features using two-dimensional(2D) digital photographs and to assess the agreement between automatic and manual measurements.: The 2D photographs...

Capsule networks for segmentation of small intravascular ultrasound image datasets.

International journal of computer assisted radiology and surgery
PURPOSE: Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the...

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization.

Sensors (Basel, Switzerland)
Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localizatio...

Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.

PloS one
Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underutilized data. As a proof of concept, we performed a feasibility study on a colitis model treated wit...

A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique, but require large annotat...

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

The American journal of pathology
With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from...

IBA-U-Net: Attentive BConvLSTM U-Net with Redesigned Inception for medical image segmentation.

Computers in biology and medicine
Accurate segmentation of medical images plays an essential role in their analysis and has a wide range of research and application values in fields of practice such as medical research, disease diagnosis, disease analysis, and auxiliary surgery. In r...

Application of Deep Learning Networks to Segmentation of Surface of Railway Tracks.

Sensors (Basel, Switzerland)
The article presents a vision system for detecting elements of railway track. Four types of fasteners, wooden and concrete sleepers, rails, and turnouts can be recognized by our system. In addition, it is possible to determine the degree of sleeper b...