Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets.

Aims: Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets.

Methods And Results: First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts.