Amin Katouzian

TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.

TL;DR: Results show that some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that automatic lumen segmentation is possible with a precision similar to that obtained by experts.

TL;DR: Recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz are reviewed.

TL;DR: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge, and the top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function.

TL;DR: In this article, a fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in OCT scans.