Correction of systematic errors in automatically produced boundaries from low-contrast ventriculograms

Poor contrast in the apex zone and nonhomogeneous mixing of the dye with the blood in the left ventricle causes the left ventricle pixel-based classifiers operating on ventriculograms to yield boundaries which are not close to ground truth boundaries as delineated by the cardiologist. They have a mean boundary error of about 6.4 mm and an error of about 12.5 mm in the apex zone. These errors have a systematic positional and orientational bias, the boundary being under-estimated in the apex zone. This paper discusses two calibration methods: the identical coefficient and the independent coefficient to remove these systematic biases. From these methods, we constitute a combined algorithm which reduces the boundary error compared to either of the calibration methods. The algorithm, in a greedy way, computes which and how many vertices of the left ventricle boundary can be taken from the computed boundary of each method to best improve the performance. The corrected boundaries have a mean error of less than 3.5 mm with a standard deviation of 3.4 mm over the approximately 6×10⁴ vertices in the data set of 291 studies. Our methodology reduces the mean boundary error by 2.9 millimeters over the boundary produced by the classifier. We also show the calibration algorithm performs better in the apex zone where the dye is unable to reach. For end-diastole, it reduces the error in the apex zone by 8.5 millimeters over the pixel-based classifier boundaries