###### Abstract  
Left ventricle (LV) segmentation of cardiac magnetic resonance (MR) images is essential for evaluating cardiac function parameters and diagnosing cardiovascular diseases (CVDs). Accurate LV segmentation remains a challenge because of the large differences in cardiac structures in different research subjects. In this work, a network based on an encoder-decoder architecture for automatic LV segmentation of short-axis cardiac MR images is proposed. It combines UNet 3+ and Transformer to jointly predict the segmentation masks and signed distance maps (SDM). UNet 3+ can extract coarse-grained semantics and fine-grained details from full scales, while a Transformer is used to extract global features from cardiac MR images. It solves the problem of low segmentation accuracy caused by blurred LV edge information. Meanwhile, the SDM provides a shape-aware representation for segmentation. The performance of the proposed network is validated on the 2018 MICCAI Left Ventricle Segmentation Challenge dataset. The five-fold cross-validation evaluation was performed on 145 clinical subjects, and the average dice metric, Jaccard coefficient, accuracy, and positive predictive value reached 0.908, 0.834, 0.979, and 0.903, respectively, showing a better performance than that of other mainstream ones. left ventricle segmentation; UNet 3+; encoder-decoder; transformer; magnetic resonance imaging +
Footnote †: journal: Applied  
Some excellent segmentation algorithms spawned in the past decades have been caught in certain dilemmas that make them difficult to apply in clinical settings. Among them, many of the algorithms are traditional methods based on machine learning [9, 10, 11], such as thresholding [12], clustering [13], active contours [14, 15], and split-merge. Figure 2 displays the thresholding, clustering, and active contours methods. Most of them use semi-automatic methods that heavily rely on the initialization step [16, 17, 18, 19], resulting in their failure to achieve the desired results. Meanwhile, because of the rapid development of the accessibility of vast training data and computer hardware, medical image segmentation algorithms based on deep learning are becoming increasingly prevalent. In particular, Convolutional Neural Networks (CNNs) have achieved excellent results in various computer vision (CV) fields, such as image segmentation [20], object detection [21], and image classification [22]. In such a trend, CNN-based LV segmentation models such as Densely Connected Convolutional Networks (DenseNet) [23] and Fully Convolutional Neural Networks (FCN) [24] have been suggested, and have achieved good results in clinical trials. Although these networks are representative, CNN-based approaches still have some limitations in capturing global information and recovering weak texture details. To overcome this limitation, some studies have suggested Transformer-based designs that use the self-attention mechanism to construct the contextual representations. Transformers, unlike traditional CNNs, can model global contextual information by modeling the relationships  
Figure 1: Short-axis cardiac MR anatomy image. Figure 2: Traditional segmentation methods. (**a**) Thresholding. Automated contours (green) and manual contours (red). (**b**) Clustering. (**c**) Active contours. between spatially distant pixels [25]. However, both models have their drawbacks. CNN is insensitive to global features, and Transformer has the issues of high computation cost and a lack of ability to capture regional features, so the applicability of both models needs to be further improved. To solve the above problems, this study proposes a fast and automated method of cardiac LV segmentation to help facilitate the diagnosis of CVD. The network has the following two main contributions: (1) The backbone uses a combined framework of UNet 3+ [26] and Transformer, which can efficiently acquire low-level spatial features as well as high-level semantic information and can also model the global context.