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Dynamic updating atlas for heart segmentation with a nonlinear field‐based model
Author(s) -
Cai Ken,
Yang Rongqian,
Yue Hongwei,
Li Lihua,
Ou Shanxing,
Liu Feng
Publication year - 2017
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1785
Subject(s) - segmentation , atlas (anatomy) , computer science , artificial intelligence , computer vision , nonlinear system , pattern recognition (psychology) , image segmentation , medicine , physics , quantum mechanics , anatomy
Abstract Background Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas‐based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. Methods This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double‐source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field‐based model was used to effectively implement a 4D cardiac segmentation. Results The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0–2.8 mm). Conclusion Our proposed method that combines a nonlinear field‐based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to‐be‐segmented DSCT sequences.