Training a computer vision model on a 50:50 blend of synthetic and real eye images produces more reliable segmentation of the ...

As shown below, the inferred masks predicted by our segmentation model trained by the dataset appear similar to the ground truth masks. Contains fundus images and corresponding optic disc/cup ...

This repository provides an end-to-end pipeline for medical image segmentation using deep learning. Implemented in Python with TensorFlow, OpenCV, and other popular libraries, this project includes ...

Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and ...

To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional ...

The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived ...

Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed ...

Stroke infarct volume predicts patient disability and has utility for clinical trial outcomes. Accurate infarct volume measurement requires manual segmentation of stroke boundaries in ...

One of the most fundamental—and indeed one of the defining—properties of a nanomaterial is the size of its constituent components. Typically, any physical–chemical characterization of such a material ...

Machine learning and computer vision technologies based on high-resolution imagery acquired using unmanned aerial systems (UAS) provide a potential for accurate and efficient high-throughput plant ...