# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 1 Introduction  
The global incidence of Cardio Vascular Diseases (CVD) has been steadily rising, reaching an estimated prevalence of 523 million cases worldwide in 2019. This figure represents a nearly twofold increase over a span of 30 years [1]. In comparison to other CVD, ischemic heart diseases stand out as the leading cause of mortality, accounting for more than 9 million deaths in 2021 [2]. In this context, Cardiac Magnetic Resonance Imaging (CMRI) has emerged as a significant aid for the visualization and diagnosis of myocardial diseases. Its proficiency lies in accurately imaging anatomical regions, all while posing minimal risks to the patient [3]. Meanwhile, deep learning approaches for classification, detection and segmentation have garnered increasing attention and have been actively employed and investigated in the medical-image-analysis domain since the mid 2010s [4]. Multiplanar Cine-MRI sequences are extensively used in clinical practice for CMRI, providing the capability to quantify cardiac function and movement while encompassing the entire heart. This quantification is performed with the help of the short-axis (SAX) and multiple long-axis (LAX) views. The SAX view acquisition usually incorporates 8 to 12 spatial slices across multiple cardiac phases, while each LAX view acquisition covers two, three and four cardiac chambers in a single spatial slice. The intersections of their respective spatial plans are centered on the left ventricle cavity. LAX views play a crucial role in imaging atria in comparison to SAX views. Specifically, LAX views are instrumental in visualizing pathological conditions that impact this area of the heart, such as malformations and interatrial communications. Additionally, LAX views contribute to the diagnosis of diseases affecting the apical region of the heart. In contrast to the SAX view, the LAX view enables the assessment of the apical region, as illustrated in Figure 1.  
While segmentation for CMRI is a well-investigated domain, the majority of studies predominantly concentrate on SAX segmentation [5], with a particular emphasis on utilizing Cine-MRI sequences. Conversely, LAX views have received comparatively less attention, with methods developed for other image orientations, particularly in SAX [6; 7; 8; 9; 10; 11; 12]. Few studies have conducted an in-depth examination of Cine-MRI long-axis cardiac images, as exemplified by the \(\Omega\)-net method proposed by [PERSON] et al. [13]. In this work, the authors addressed multiclass segmentation for SAX, LAX two-chamber and four-chamber views by employing a predelineation UNet. The bottom features from this UNet were utilized to train a Spatial Transformer Network [14], allowing the learning and estimation of rigid affine transformation matrix parameters to achieve a canonical orientation consistent with clinical practices across the dataset. Following this transformation, the output was directed to a second segmentation pipeline comprising multiple chained UNets. For LAX four-chamber images, the authors annotated over five classes in addition to the background class, facilitating whole-heart segmentation. This study involved full cardiac cycle annotation. The reported results include average Intersection over Union (IoU) scores of 0.856 and 0.845 for LAX two-chamber and LAX four-chamber, respectively. Another study involving fully annotated LAX four-chamber was conducted by [PERSON] et al. [15]. In this work, the authors compared a semisupervised learning pipeline to a baseline UNet by using a small amount of annotated data. They reached average respective Dice scores of \(0.934\pm 0.029\) and \(0.930\pm 0.032\) by using 200 training slices corresponding to ED and ES timeframes for 100 examinations. Their approach introduces a distinct starting postulate, considering the various axes of view as entangled. The idea is that these axes can be learned through shared anatomical features at their intersections, thereby enhancing segmentation by incorporating surrounding features.