A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI

Abstract

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic segmentation tool for the LV from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus those of 79.2% − 95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other methods, respectively.

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…”

“…Such algorithms are being actively researched; the papers [8][9][10] declared segmentation accuracy comprising average dice metric value of 0.9+ for inner and outer contours of LV on Sunnybrook dataset. We expect that DBN-based approaches are the most promising ones because the main training process of DBN can be performed on the unlabeled data.…”

“…Hence, a wide variety of segmentation techniques has been developed specifically for various type of applications, including cardiac segmentation. Many completely different principles have been adopted, including heuristics and basic machine learning methods [6,7], deep learning [8,9], deformable models [10], and random forests [11]. Segmentation was even treated as a tracking problem and several complex statistical models have been proposed [12].…”

“…For instance, the energy can be regularized by the distance from the deformable contour to one retrieved by other approach, as described in [9]. Several recent researches, which declared state-of-art results (average DM value of 0.9-0.95), used combination of deformable models with another machine learning algorithms, such as DBN [9], CNN [8], or Markov random fields [10].…”

A Novel Left Ventricular Volumes Prediction Method Based on Deep Learning Network in Cardiac MRI

“…The left ventricle (LV) is the biggest chamber of heart, and the function indexes from LV have important indication significance for cardiac diseases, such as myocardial hypertrophy, myocardial ischemia, heart failure and so on. In recent years, more and more researches focus on the LV, especially in the field of computer-aided medicine [3][4][5]. The related LV function indexes, such as the end-diastole volumes (EDV), the end-systole volumes (ESV), and the eject fraction (EF), can be estimated from cardiovascular MR (CMR) images.…”