Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking
In this study, we have tailored a pixel tracking method for temporal extrapolation of the ventricular segmentation masks in cardiac magnetic resonance images. The pixel tracking process starts from the end-diastolic frame of the heart cycle using the available manually segmented images to predict the end-systolic segmentation mask. The superpixels approach is used to divide the raw images into smaller cells and in each time frame, new labels are assigned to the image cells which leads to tracking the movement of the heart wall elements through different frames. The tracked masks at the end of systole are compared with the already available manually segmented masks and dice scores are found to be between 0.81 to 0.84. Considering the fact that the proposed method does not necessarily require a training dataset, it could be an attractive alternative approach to deep learning segmentation methods in scenarios where training data are limited.
Code (1)
Tasks
SegmentationSuperpixelsSimilar Papers 제목 키워드 기반
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation. Many cardiac image segmentation methods …
Image SegmentationSegmentationSemantic SegmentationCardiac MR Image Segmentation Techniques: an overview
Broadly speaking, the objective in cardiac image segmentation is to delineate the outer and inner walls of the heart to segment out either the entire or parts of the organ boundaries. This paper will focus on MR images a…
Cardiac SegmentationImage SegmentationMedical Image SegmentationSegmentation+1Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentation
Current cardiac cine magnetic resonance image (cMR) studies focus on the end diastole (ED) and end systole (ES) phases, while ignoring the abundant temporal information in the whole image sequence. This is because whole …
AnatomyMRI segmentationSegmentationSemantic Segmentation+2Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning
Automated noninvasive cardiac diagnosis plays a critical role in the early detection of cardiac disorders and cost-effective clinical management. Automated diagnosis involves the automated segmentation and analysis of ca…
Deep LearningSegmentationSemantic SegmentationHeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation
Accurate patient-specific tetrahedral cardiac meshes are essential for in-silico trials, yet common segmentation-then-modelling pipelines can blur thin-wall anatomy and offer limited cross-case correspondence. We propose…