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An Improved Level Set Algorithm Based on Prior Information for Left Ventricular MRI Segmentation

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An Improved Level Set Algorithm Based on Prior Information for Left Ventricular MRI Segmentation electronics Article An Improved Level Set Algorithm Based on Prior Information for Left Ventricular MRI Segmentation Lei Xu * , Yuhao Zhang , Haima Yang and Xuedian Zhang School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; [email protected] (Y.Z.); [email protected] (H.Y.); [email protected] (X.Z.) * Correspondence: [email protected] Abstract: This paper proposes a new level set algorithm for left ventricular segmentation based on prior information. First, the improved U-Net network is used for coarse segmentation to obtain pixel-level prior position information. Then, the segmentation result is used as the initial contour of level set for fine segmentation. In the process of curve evolution, based on the shape of the left ventricle, we improve the energy function of the level set and add shape constraints to solve the “burr” and “sag” problems during curve evolution. The proposed algorithm was successfully evaluated on the MICCAI 2009: the mean dice score of the epicardium and endocardium are 92.95% and 94.43%. It is proved that the improved level set algorithm obtains better segmentation results than the original algorithm. Keywords: left ventricular segmentation; prior information; level set algorithm; shape constraints Citation: Xu, L.; Zhang, Y.; Yang, H.; Zhang, X. An Improved Level Set Algorithm Based on Prior 1. Introduction Information for Left Ventricular MRI Uremic cardiomyopathy is the most common complication also the cause of death with Segmentation. Electronics 2021, 10, chronic kidney disease and left ventricular hypertrophy is the most significant pathological 707. https://doi.org/10.3390/ feature of uremic cardiomyopathy [1]. Therefore, it is of great significance for the prevention electronics10060707 and treatment of uremic diseases to segment left ventricle from medical images and analyze its pathology scientifically, objectively and quantitatively. Heart and other soft tissue Academic Editors: Luca Mesin and images have low contrast with the background and high noise [2], so segmentation of Byung Cheol Song left ventricle has always been a difficult problem in the field of image segmentation. In recent years, image segmentation technology based on deep convolution neural network is Received: 6 February 2021 widely used in various medical image segmentation, such as MRI, CT and X-ray. Comelli Accepted: 16 March 2021 Published: 18 March 2021 et al. [3] had proved that using deep learning to assist medical segmentation can not only improve the accuracy of diagnosis result, but also improve the management of Publisher’s Note: MDPI stays neutral patients towards personalized risk strategies. However, fully convolution network (FCN), with regard to jurisdictional claims in U-Net and other segmentation models of deep convolution neural network are pixel published maps and institutional affil- level segmentation [4]. For some medical images with sub-pixel segmentation accuracy, iations. there are still some shortcomings. The level set algorithm based on curve evolution and contour fitting can achieve sub-pixel segmentation effect and the segmentation result is more accurate. However, the level set algorithm needs to set the initial contour artificially. Because the contour of tissues and organs in medical images is fuzzy, it is difficult to achieve pixel level accuracy by human calibration of the initial contour, which is easy Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. to cause evolution error. Moreover, for the sequential medical images, each layer of This article is an open access article tissue section is to calibrate the initial contour again, which undoubtedly increases the distributed under the terms and workload of doctors. Therefore, this paper proposes a level set algorithm based on prior conditions of the Creative Commons information to segment the left ventricle. We use a convolution neural network to extract Attribution (CC BY) license (https:// the deep information of the image and provide pixel level initial contour for the region creativecommons.org/licenses/by/ to be segmented. Then, we use a level set algorithm based on prior shape constraint to 4.0/). segment the left ventricle in detail and obtain sub-pixel level segmentation results. The Electronics 2021, 10, 707. https://doi.org/10.3390/electronics10060707 https://www.mdpi.com/journal/electronics Electronics 2021, 10, 707 2 of 13 experimental results on MICCAI 2009 dataset show that our segmentation algorithm is better than other segmentation algorithms. 2. Related Works Since Osher et al. [5] proposed the level set algorithm, it has made great achievements in the field of medical image segmentation. The level set algorithm mainly considers how to fuse the image information into the construction of energy functional, so as to segment the image effectively. The Mumford-Shah (MS) [6] model was proposed by Mumford and Shah, which aimed at image segmentation by minimizing the energy function. Because of the smooth term and regular term in the model, the segmentation result was smoother and more accurate, but the solution of the model was more complex. Chan and Vese simplify the solution process of the MS model by improving the smoothing term, which was the famous CV model [7]. Although CV model solved the defects of MS model, it needed to re-initialize the contour in the iterative process of CV model. Li et al. [8] proposed a level set model without reinitialization, which further improved the wide application of level set algorithm in medical image segmentation. In order to further solve the problem of incorrect moving of contour line and error segmentation result caused by weak boundary and uneven gray level in medical image, a series of excellent models were proposed to make level set have higher anti-noise performance in medical image segmentation. In recent years, with the improvement of computational power, convolutional neural network had been gradually applied to medical image segmentation. FCN was first proposed for image segmentation, FCN also achieved good segmentation results in medical images [9]. Subsequently, U-Net innovatively proposed up sampling and feature fusion technology, which had been widely recognized in medical image segmentation [10]. On the basis of U-Net, many had proposed the improved segmentation network [11–13]. Although these neural networks can obtain more accurate segmentation results, due to the limited number of specific data sets and the segmentation results were not smooth enough, as we can see from Figure1, neural networks still cannot obtain sub-pixel seg- mentation results in actual medical image segmentation. Recently, some scholars had tried to combine level set algorithm with neural network to get better segmentation results. Kim et al. [14] proposed that the energy function of level set was directly used as the loss function of neural network. This method effectively used the contour information of the target area to be segmented and made up for the shortcomings of neural network. Hatamizadeh et al. [15] also used neural network to learn the energy function of level set. The difference is that they added distance regularization change to initialize the level set contour, so they obtained better segmentation results. Based on this idea, Kim [16] pro- posed a semi supervised segmentation method, using the energy function of the level set as the loss function and using some unlabeled data for training, which also obtained good segmentation results. Chen et al. [17] used the length regular term and area regular term in the level set as constraints to modify the segmentation results of neural network, but only took the length and area of the target region as constraints, without considering the single integrity of the target region, it is easy to cause error segmentation. The above method of applying level set energy function to convolutional neural network is still the result of pixel level segmentation in essence, which cannot reach sub-pixel level. Recently, Comelli [18] added the classification results of machine learning as constraints to the energy function of level set to segment medical images and achieved good results. Thus, the combination of neural network and level set algorithm is not only feasible, but also may get better results. Electronics 2021, 10, 707 3 of 13 Electronics 2021, 10, x FOR PEER REVIEW 3 of 13 Electronics 2021, 10, x FOR PEER REVIEW 3 of 13 Figure 1. Comparison of U-Net segmentation and level set evolution results (U-Net segmentation results on the left and Figure 1. ComparisonComparison of of U-Net U-Net segmentation segmentation and and level level set set evolution evolution results results (U-Net (U-Net segmentation segmentation results results on the on left the and left and level set evolution results on the right). level set set evolution evolution results results on on the the right). right). 3. Proposed Method for Left Ventricular Segmentation 3.3. Proposed Proposed Method Method for for Left Left Ventricular Ventricular Segmentation Segmentation InInIn this this this paper, paper, paper, we we propose propose a levela level set setalgorithm algorithm based based on location on location and andshape shape prior prior informationinformationinformation to to to segment segment segment left left left ventricular ventricular ventricular endocardium endocardium and and epicardium. epicardium. It is Itmainly is mainly mainly divided divided divided intointointo
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