Towards ultrasound-based navigation: deep learning based IVC lumen segmentation from intracardiac echocardiography

Vascular navigation is a prerequisite to transcatheter cardiac interventions. The current standard approach to catheter navigation relies on real-time fluoroscopy, while this technique utilizes ionizing radiation and it places the interventionalist at risk for eye cataracts and cancer. The shielding equipment needed to mitigate these risks is associated with spinal issues and neck and back pain which has led towards the coining of the term “interventionalist’s disc disease”. A proposed alternative is to have an ultrasound-guided vascular navigation system where a catheter-based ultrasound probe scans the vessel and reconstructs the vascular roadmap, which can then be navigated by tracked guidewire or catheter. One of the major challenges here is the segmentation of the vessel lumen from the ultrasound images. In this study, we address this challenge using a deep learning based approach. We acquired inferior vena cava (IVC) images from an animal study performed using a radial, forward-looking Foresight intracardiac echocardiography (ICE) ultrasound probe. The ground truth was established using manual segmentations and validated by an expert clinician. We use the MONAI platform to train a U-net architecture on our dataset to perform vessel segmentation. The images are cropped to retain only the central 300 pixels as the traversed vessel will always appear central to the radial ICE image. Data augmentation was performed to enhance the number of images available for training. After post-processing, the segmentation output, a 90 % accuracy was achieved as indicated by the Dice coefficient. We plan on integrating this vessel segmentation pipeline in an image-guided surgical navigation system.

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