From 2D Ultrasound to Patient-Specific 3D Surface Models for Interventional Guidance.

Abstract

Ultrasound exam output of large organs like liver has traditionally been limited to still images or 2D cine loops of key structures, without 3D context. Having 3D context for follow up studies makes ultrasound scanning much easier and for interventional applications such as biopsy. 3D context will reduce wrong sample selection thereby increasing patient comfort. As of today, there is no existing solution which provides 3D anatomical context to users during scanning for large organs like liver. Even for routine measurements like liver volume, patients have to undergo CT or MR scan. In this paper, we propose a novel approach to build-patient specific 3D anatomical surface models from B-mode ultrasound images and tracking information from position sensors. The complexity of the problem stems from the fact that liver boundaries are often not very clear in ultrasound images, in addition to large variability in liver size and shape across patients. Our work uses state-of-the-art deep learning algorithms to detect surface landmarks of liver followed by registering a geometric model to surface point cloud to build patient specific 3D liver model. Further, the developed models will be used to guide users to right lesion locations during the interventional procedure. Our proposed semi -automated workflow ensures the accuracy of the developed models are within acceptable limits for the targeted problem.