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

TL;DR

This paper introduces a novel LV segmentation method combining SDD double threshold selection and CHT, achieving high accuracy in MRI images, surpassing recent methods.

Contribution

The paper presents a robust LV segmentation approach that integrates SDD double thresholding with CHT, improving accuracy over existing techniques.

Findings

Achieved 96.51% DICE score on ACDC dataset

Outperformed recent state-of-the-art methods

Demonstrated robustness in MRI LV segmentation

Abstract

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.