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