a semi-automated algorithm for segmentation of the left atrial appendage landing zone: application in left atrial appendage occlusion procedures

Background: mechanical occlusion of the left atrial appendage (laa) using a purpose-built device has emerged as an effective prophylactic treatment in patients with atrial fibrillation at risk of stroke and a contraindication for anticoagulation. a crucial step in procedural planning is the choice of the device size. this is currently based on the manual analysis of the “device landing zone” from echocardiographic images. objective: we aimed to develop an algorithm for automated segmentation of the laa landing zone from 3d echocardiographic images of the laa. material and methods: in this experimental study, 2d axial images were derived from the 3d echo datasets. after image pre-processing, binary images were created using a thresholding method. a binary image matrix was then formed and scanned using 8-adgacency approach resulting in segmentation of the objects with a closed circumference within the image. erosion/dilation techniques were then applied to remove small objects. a feature-based approach was then used to firstly detect the laa region and secondly to identify the device landing zone. results: a total of 22 datasets were used in this study. the algorithm produced up to 9 axial images as the proposed landing zone. the selected axial images were compared to the echocardiographic images. in 18 cases (81.8%), the algorithm successfully segmented the laa and proposed the landing zone based on the defined features. conclusion: we have developed a simple and fast algorithm for semi-automated segmentation of the laa landing zone. further studies are needed to assess the accuracy of the proposed landing zones by this method.