Segmentation of the Left Ventricle by SDD double threshold selection and CHT
This work addresses a challenging medical imaging task for cardiac diagnosis, but it appears incremental as it builds on existing techniques like SDD and CHT rather than introducing a novel paradigm.
The paper tackled the problem of automatic left ventricle segmentation in MRI by proposing a method based on slope difference distribution double threshold selection and circular Hough transform, achieving a 96.51% DICE score on the ACDC test set, which is higher than previously reported accuracies.
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.