Slice-to-volume deformable registration: efficient one-shot consensus between plane selection and in-plane deformation

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• 作者：Enzo Ferrante ; Vivien Fecamp ; Nikos Paragios
• 关键词：Slice ; to ; volume registration ; 2D-D registration ; Discrete optimization ; Graphical models ; Markov random fields
• 刊名：International Journal of Computer Assisted Radiology and Surgery
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：10
• 期：6
• 页码：791-800
• 全文大小：1,595 KB
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• 作者单位：Enzo Ferrante (1)
  Vivien Fecamp (1)
  Nikos Paragios (1)
  
  1. Center for Visual Computing (CVN), CentraleSupelec -Galen Team, INRIA, 92295, Chatenay-Malabry, France
  
• 刊物主题：Imaging / Radiology; Surgery; Health Informatics; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science, general;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-6429

文摘

Purpose This paper introduces a novel decomposed graphical model to deal with slice-to-volume registration in the context of medical images and image-guided surgeries. Methods We present a new non-rigid slice-to-volume registration method whose main contribution is the ability to decouple the plane selection and the in-plane deformation parts of the transformation—through two distinct graphs—toward reducing the complexity of the model while being able to obtain simultaneously the solution for both of them. To this end, the plane selection process is expressed as a local graph-labeling problem endowed with planarity satisfaction constraints, which is then directly linked with the deformable part through the data registration likelihoods. The resulting model is modular with respect to the image metric, can cope with arbitrary in-plane regularization terms and inherits excellent properties in terms of computational efficiency. Results The proof of concept for the proposed formulation is done using cardiac MR sequences of a beating heart (an artificially generated 2D temporal sequence is extracted using real data with known ground truth) as well as multimodal brain images involving ultrasound and computed tomography images. We achieve state-of-the-art results while decreasing the computational time when we compare with another method based on similar techniques. Conclusions We confirm that graphical models and discrete optimization techniques are suitable to solve non-rigid slice-to-volume registration problems. Moreover, we show that decoupling the graphical model and labeling it using two lower-dimensional label spaces, we can achieve state-of-the-art results while substantially reducing the complexity of our method and moving the approach close to real clinical applications once considered in the context of modern parallel architectures.