Right Ventricle Segmentation by Temporal Information Constrained Gradient Vector Flow

Abstract

Evaluation of right ventricular (RV) structure and function is of importance in the management of most cardiac disorders. But the segmentation of RV has always been considered challenging due to low contrast of the myocardium with surrounding and high shape variability of the RV. In this paper, we present a 2D + T active contour model for segmentation and tracking of RV endocardium on cardiac magnetic resonance (MR) images. To take into account the temporal information between adjacent frames, we propose to integrate the time-dependent constraints into the energy functional of the classical gradient vector flow (GVF). As a result, the prior motion knowledge of RV is introduced in the deformation process through the time-dependent constraints in the proposed GVF-T model. A weighting parameter is introduced to adjust the weight of the temporal information against the image data itself. The additional external edge forces retrieved from the temporal constraints may be useful for the RV segmentation, such that lead to a better segmentation performance. The effectiveness of the proposed approach is supported by experimental results on synthetic and cardiac MR images.

A review of approaches investigated for right ventricular segmentation using short‐axis cardiac MRI

“…Integrating time-dependent constraints into the energy function of the classical GVF, Yang et al proposed a 2D+t AC model [27]. Based on the "dt" model defined in Equation (10), the temporal information for a frame at time t was defined between adjacent frames; "the previous K frames at time t − i, the next K frames at time t + i, where K is an integer parameter denoting the time offset between the frames".…”

Right Ventricle Segmentation in Cardiac MR Images Using Convolutional Neural Network Architecture

“…A number of researchers have chosen to use traditional segmentation algorithms. Deformable models are widely used [4], [5], [6], [7] due to their efficient detection of boundaries and variation in shape or complex geometry. Arrieta et al suggested a TPLS technique based on the saved structure level sets using a restricted base of 35 pathological patients and 6 healthy cases.…”

Accuracy Assessment of a Deep-Learning Based Segmentation Tool Over Right Ventricle Short-Axis Slices

“…Since atlas-based methods depend at the first stage on the database used, its results may fail to provide high accuracy when the segmented cardiac images present different structures from those used in the first place. Deformable models are also used for RV segmentation such in [9] and [10]. These methods are based on the shape information of the right ventricular.…”