Automatic Segmentation of the Left Ventricle in Cardiac MRI Using Local Binary Fitting Model and Dynamic Programming Techniques

Abstract

Segmentation of the left ventricle is very important to quantitatively analyze global and regional cardiac function from magnetic resonance. The aim of this study is to develop a novel algorithm for segmenting left ventricle on short-axis cardiac magnetic resonance images (MRI) to improve the performance of computer-aided diagnosis (CAD) systems. In this research, an automatic segmentation method for left ventricle is proposed on the basis of local binary fitting (LBF) model and dynamic programming techniques. The validation experiments are performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 93.5%, the average perpendicular distance are about 2 mm. The overlapping dice metric is about 0.91. The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF). The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.

An Overview of Techniques for Cardiac Left Ventricle Segmentation on Short-Axis MRI

“…Average dice metric CNN and deformable models [8] 0.93 DBN and deformable models [9] 0.90 Heuristics and deformable models [19] 0.91 CNN standalone [17] 0.86…”

“…A combination of complex heuristics and deformable model, described in [19], preforms not as good as aforementioned combinations with advanced machine learning, yet it is quite competitive with DM value 0.91 on Sunnybrook dataset.…”

Automatic Segmentation of Left Ventricular Myocardium by Deep Convolutional and De:convolutional Neural Networks

“…Hu et al proposed an automatic LV myocardium segmentation using local binary fitting and dynamic programming [7], producing a DM result of 0.89±0.04 and 0.93±0.02 for the endocardium and epicardium segmentation, respectively. Uzunbas et al performed a semi-automatic LV myocardium segmentation using graph cut and deformable models [8].…”

Fully automatic cardiac T2* relaxation time estimation using marker-controlled watershed

“…There are many previous works proposed as the methods for automatic segmentation in MR images which focus on different organs such as brain, kidneys, liver, and heart [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

“…The other is necessary to use some training datasets, and is called segmentation with strong prior [8]. The examples of segmentation algorithms are thresholding [8,9], region growing [10], dynamic programming (DP) [11], deformable models [12], graph cuts [12], active contour models (ACM) [13][14], level-set [15], KNN classifier [16], convex relaxed distribution matching [17], robust adaptive Gaussian regularizing Chan-Vese (CV) model [18], and clustering [19]. Most of the previous works were focused on automatic LV segmentation for evaluating the cardiac function, but only a few works were focused on cardiac T2* estimation.…”