학술논문

Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets
Document Type
Periodical
Source
IEEE Transactions on Medical Imaging IEEE Trans. Med. Imaging Medical Imaging, IEEE Transactions on. 34(7):1460-1473 Jul, 2015
Subject
Bioengineering
Computing and Processing
Image segmentation
Computed tomography
Magnetic resonance imaging
Shape
Educational institutions
Measurement
Benchmark testing
benchmark testing
magnetic resonance imaging
computed tomography
cardiovascular disease
left atrium
Language
ISSN
0278-0062
1558-254X
Abstract
Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM’13 workshop, in conjunction with MICCAI’13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.