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.

翻译：在磁共振图像中对左心室进行自动且鲁棒的分割数十年来一直充满挑战。随着深度学习在目标检测与分类领域的巨大成功，近年来左心室分割的研究重点已转向卷积神经网络。然而，左心室分割属于像素级分类问题，其类别复杂度远超目标检测与分类任务。本文提出了一种基于斜率差分布双阈值选择与圆形霍夫变换的鲁棒左心室分割方法。该方法在自动心脏诊断挑战测试集上取得了96.51%的DICE分数，高于近期文献中报告的最佳准确率。