A semi-automatic and automatic approach successfully segmented the left atrium and pulmonary veins from 20 patient MRA datasets to assist in radio-frequency catheter ablation.
The proposed segmentation algorithms offer a method to automatically identify the left atrium body and pulmonary vein ostia from MRA data, which may assist in pre-operative planning for atrial fibrillation ablation.
Segmentation of the left atrium is vital for pre-operative assessment of its anatomy in radio-frequency catheter ablation (RFCA) surgery. RFCA is commonly used for treating atrial fibrillation. In this paper we present an semi-automatic approach for segmenting the left atrium and the pulmonary veins from MR angiography (MRA) data sets. We also present an automatic approach for further subdividing the segmented atrium into the atrium body and the pulmonary veins. The segmentation algorithm is based on the notion that in MRA the atrium becomes connected to surrounding structures via partial volume affected voxels and narrow vessels, the atrium can be separated if these regions are characterized and identified. The blood pool, obtained by subtracting the pre- and post-contrast scans, is first segmented using a region-growing approach. The segmented blood pool is then subdivided into disjoint subdivisions based on its Euclidean distance transform. These subdivisions are then merged automatically starting from a seed point and stopping at points where the atrium leaks into a neighbouring structure. The resulting merged subdivisions produce the segmented atrium. Measuring the size of the pulmonary vein ostium is vital for selecting the optimal Lasso catheter diameter. We present a second technique for automatically identifying the atrium body from segmented left atrium images. The separating surface between the atrium body and the pulmonary veins gives the ostia locations and can play an important role in measuring their diameters. The technique relies on evolving interfaces modelled using level sets. Results have been presented on 20 patient MRA datasets.
Karim et al. (Thu,) conducted a other in Atrial fibrillation (n=20). Semi-automatic and automatic segmentation algorithms was evaluated on Segmentation of the left atrium and pulmonary veins. A semi-automatic and automatic approach successfully segmented the left atrium and pulmonary veins from 20 patient MRA datasets to assist in radio-frequency catheter ablation.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: