PURPOSE: Cardiac computed tomography (CT) is widely used in clinical diagnosis of @entity65 . Whole @entity299 (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the @entity1347 challenging. The objective of this work is to develop and evaluate a @entity1347 ( @entity1347 ) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different @entity1347 strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance. METHODS: Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors' proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed @entity1347 scheme and to investigate different segmentation strategies. RESULTS: The mean WHS Dice score of the proposed @entity1347 method was 0.918    0.021, and the mean runtime for one case was 13.2 min on a workstation. This @entity1347 scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901    0.276, p < 0.01), locally weighted voting (0.905    0.0247, p < 0.01), and probabilistic patch-based fusion (0.909    0.0249, p < 0.01). In the atlas ranking study, the proposed criterion based on conditional entropy yielded a performance curve with higher WHS Dice scores compared to the conventional schemes (p < 0.03). In the atlas database study, the authors showed that the @entity1347 using larger atlas databases generated better performance curves than the @entity1347 using smaller ones, indicating larger atlas databases could produce more accurate segmentation. CONCLUSIONS: The authors have developed a new @entity1347 framework for automatic WHS of CTA and investigated alternative implementations of @entity1347 . With the proposed atlas ranking algorithm and joint label fusion, the @entity1347 scheme is able to generate accurate segmentation within practically acceptable computation time. This method can be useful for the development of new clinical applications of cardiac CT.

Multiatlas whole XXXX of CT data using conditional entropy for atlas ranking and selection.

@entity1347 :: ('MESH:C537775', 'Disease') :: ['multiatlas segmentation', 'MAS', 'automatic segmentation particularly'] @entity65 :: ('MESH:D002318', 'Disease') :: ['cardiovascular diseases'] @entity299 :: ('MESH:D006331', 'Disease') :: ['heart segmentation']
@entity299 :: (MESH:D006331,Disease) :: ['heart segmentation']
