AIMC Topic: Image Processing, Computer-Assisted

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Reconstruction of Organ Boundaries With Deep Learning in the D-Bar Method for Electrical Impedance Tomography.

IEEE transactions on bio-medical engineering
OBJECTIVE: Medical electrical impedance tomography is a non-ionizing imaging modality in which low-amplitude, low-frequency currents are applied on electrodes on the body, the resulting voltages are measured, and an inverse problem is solved to deter...

Self-augmentation: Generalizing deep networks to unseen classes for few-shot learning.

Neural networks : the official journal of the International Neural Network Society
Few-shot learning aims to classify unseen classes with a few training examples. While recent works have shown that standard mini-batch training with carefully designed training strategies can improve generalization ability for unseen classes, well-kn...

A deep learning approach with temporal consistency for automatic myocardial segmentation of quantitative myocardial contrast echocardiography.

The international journal of cardiovascular imaging
Quantitative myocardial contrast echocardiography (MCE) has been proved to be valuable in detecting myocardial ischemia. During quantitative MCE analysis, myocardial segmentation is a critical step in determining accurate region of interests (ROIs). ...

Adaptive optics for structured illumination microscopy based on deep learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. However, when imaging thick samples, the structured illumination patterns in SIM will suffer from opt...

An adaptive digital stain separation method for deep learning-based automatic cell profile counts.

Journal of neuroscience methods
BACKGROUND: Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in the fields of pathology, toxicology, cancer and behavior. As part of a program to develop accurate, precise an...

Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN.

Computers in biology and medicine
Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocephalus and is correlated with future neuro-developmental impairments. Con...

GreenSea: Visual Soccer Analysis Using Broad Learning System.

IEEE transactions on cybernetics
Modern soccer increasingly places trust in visual analysis and statistics rather than only relying on the human experience. However, soccer is an extraordinarily complex game that no widely accepted quantitative analysis methods exist. The statistics...

Multimodal Learning of Social Image Representation by Exploiting Social Relations.

IEEE transactions on cybernetics
Learning the representation for social images has recently made remarkable achievements for many tasks, such as cross-modal retrieval and multilabel classification. However, since social images contain both multimodal contents (e.g., visual images an...

Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery.

Scientific reports
In this work, we developed and validated a computer method capable of robustly detecting drill breakthrough events and show the potential of deep learning-based acoustic sensing for surgical error prevention. Bone drilling is an essential part of ort...

Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays.

Practical radiation oncology
Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (D...