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Automatic and robust segmentation of the left ventricle (LV) in magneticresonance images (MRI) has remained challenging for many decades. With thegreat success of deep learning in object detection and classification, theresearch focus of LV segmentation has changed to convolutional neural network(CNN) in recent years. However, LV segmentation is a pixel-level classificationproblem and its categories are intractable compared to object detection andclassification. In this paper, we proposed a robust LV segmentation methodbased on slope difference distribution (SDD) double threshold selection andcircular Hough transform (CHT). The proposed method achieved 96.51% DICE scoreon the test set of automated cardiac diagnosis challenge (ACDC) which is higherthan the best accuracy reported in recently published literatures.

Segmentation of the Left Ventricle by SDD double threshold selection and CHT