Structured Decision Forests For Multi-modal Ultrasound Image Registration

Ozan Oktay, Andreas Schuh, Martin Rajchl, Kevin Keraudren, Alberto Gomez, Mattias Heinrich, Graeme Penney, Daniel Rueckert


Interventional procedures in cardiovascular diseases often require ultrasound (US) image guidance. These US images must be combined with pre-operatively acquired tomographic images to provide a roadmap for the intervention. Spatial alignment of pre-operative images with intra-operative US images can provide valuable clinical information. Existing multi-modal US registration techniques often do not achieve reliable registration due to low US image quality. To address this problem, a novel medical image representation based on a trained decision forest named probabilistic edge map (PEM) is proposed in this paper. PEMs are generic and modality-independent. They generate similar anatomical representations from different imaging modalities and can thus guide a multi-modal image registration algorithm more robustly and accurately. The presented image registration framework is evaluated on a clinical dataset consisting of 10 pairs of 3D US-CT and 7 pairs of 3D US-MR cardiac images. The experiments show that a registration based on PEMs is able to estimate more reliable and accurate inter-modality correspondences compared to other state-of-the-art US registration methods.
TitelMedical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Redakteure/-innenNassir Navab, Joachim Hornegger, William M. Wells, Alejandro Frangi
Herausgeber (Verlag)Springer Vieweg, Berlin Heidelberg
ISBN (Print)978-3-319-24570-6
ISBN (elektronisch)978-3-319-24571-3
PublikationsstatusVeröffentlicht - 20.11.2015
Veranstaltung18th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015
- Munich, Deutschland
Dauer: 05.10.201509.10.2015


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