# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 1 Introduction  
Cardiac image segmentation is a laborious manual task which requires both working hours of the radiologist and accurate analysis. Therefore, the development of automatic methods for cardiac image analysis is of major importance [1]. This demonstrates the high potential of deep learning-based segmentation methods, for applications where manual or semiautomatic contouring is still a bottleneck in the workflow [2]. While deep learning methods have recently shown great promise for the task of cardiac image segmentation [1], one of their most significant drawbacks is their need for a large amount of data to provide good results. There are a number of publicly available medical image datasets [1], among which the most popular being Multi-Modality Whole Heart Segmentation--MMWHS [3] and the Automated Cardiac Diagnosis challenge--ACDC [4]. However, in most cases, the data are limited in numbers (i.e., couple of hundreds at most), since medical data acquisition, and cardiac Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) are especially difficult to perform on a large population [5].  
Several methods for handling the scarceness of labeled images have been proposed [6, 7], but they can address the problem of limited annotations only partially. For instance, other methodologies (CT-based) to apply semi- and fully automated imageanalysis are proposed in [8; 9], where the importance of epicardial fat segmentation on Computed Tomography are analyzed by taking into account either the classic image-processing methods or the deep learning methods. Furthermore, image acquisition methods play an important role in medical image segmentation. It has been shown in [1; 10] that factors such as slice thickness and large inter-slice gaps can determine 2D approaches to work better than 3D ones.  
In this paper, we study the feasibility of applying deep learning methods for the problem of cardiac image segmentation on a pilot dataset. We attempt to make the most out of the available data via a number of techniques, which include: data augmentation, transfer learning, region of interest (ROI) localization, and ensembling. The pipeline we apply processes volumes in an entirely 2D, slice-by-slice manner, obtaining promising results for a possible clinical pipeline application.  
We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general purpose model, DeepLabV3+. Novel modifications and integrations to U-Net (like those proposed in [11; 12; 13]) will be explored in the future. In [14] a deep learning-based segmentation method that automatically configures itself (including preprocessing, network architecture, training, and post-processing), called nnU-net, is proposed for a large range of segmentation tasks in the biomedical domain. Although nnU-Net has been shown to find high-quality configurations on new datasets robustly, task-specific empirical optimization may have the potential to further enhance the performance of segmentation. We will show how our adapted pipeline is able to better solve the investigated problem.  
The structure of this work is as follows: Section 2 covers previous relevant research on the topic, Section 3 presents the datasets used for the experiments, Section 4 is dedicated to the proposed segmentation method, while Section 5 summarizes the details of the performed experiments and results. Finally, Section 6 provides the conclusion.