论文标题
SDD双阈值选择和CHT对左心室的分割
Segmentation of the Left Ventricle by SDD double threshold selection and CHT
论文作者
论文摘要
磁共振图像（MRI）中左心室（LV）的自动分割（MRI）数十年来一直具有挑战性。随着深度学习在对象检测和分类中的巨大成功，LV分割的研究重点已变为近年来卷积神经网络（CNN）。但是，LV分割是一个像素级分类问题，与对象检测和分类相比，其类别是棘手的。在本文中，我们提出了一种基于斜率差分布（SDD）双阈值选择和圆形霍夫变换（CHT）的强大LV分割方法。所提出的方法在自动化心脏诊断挑战（ACDC）测试集（ACDC）上达到了96.51％的骰子得分，该方法高于最近发表的文献中报告的最佳准确性。
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.
