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 ...

If there is one thing the COVID-19 pandemic has taught us is that wearing a mask is of paramount importance. However, in a world full of fingerprint sensors in phones, it’s easy to see how iPhone ...

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 ...

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 ...

Despite great advances in brain tumor segmentation and clear clinical need, translation of state-of-the-art computational methods into clinical routine and scientific practice remains a major ...