Detection of the myocardial boundary in the left ventricle from simultaneously acquired triplane ultrasound images using multi view active appearance motion models

Abstract

We report a new algorithm for detecting the LV myocardial boundary from simultaneously acquired triplane US image sequences using Multi View Active Appearance Motion Models. Coupled boundary detection in three planes can po- tentially increase the accuracy of LV volume measurements, and also increase the robustness of the boundary detection over traditional methods. A database of triplane image sequences from full cardiac cycles, including the standard A4CH, A2CH, and ALAX views were established from 20 volunteers, including 12 healthy persons and 8 persons suffering from heart disease. For each dataset the LV myocardial boundary was manually outlined, and the ED and ES frames were determined visually for phase normalization of the cycles. The evaluation of the MVAAMM was performed using a leave one out approach. The mean point distance between manually and automatically determined contours were 4.1±1.9 mm, the volume error was 7.0±14 ml, and fractional volume error was 8.5±16 %. Volume detection using the automatic method showed excellent correlation to the manual method (R 2 =0.87). Common ultrasound artefacts such as dropouts were handled well by the MVAAMM since the detection in the three image planes were coupled. The views with the largest point distance had one or more foreshortened views. A larger training database may improve the performance in such cases.