# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 1 Introduction  
In recent years, magnetic resonance (MR) imaging has gained increasing relevance in cardiology due to its high image resolution, superior contrast, and radiation-free nature [1]. Cardiac magnetic resonance (CMR) is now indispensable in clinical practice [2] and is widely recognized as the gold standard for assessing cardiac chamber volumes, cardiac tissue health, and myocardial viability. A CMR exam begins with the acquisition of localizer images in the axial, coronal, and sagittal planes to align the MR setup with the patient's position. These initial images then guide the acquisition of multi-slice sequences along the same directions, known as anatomical planes, which provide a comprehensive viewof the heart and facilitate the definition of cardiac axes for subsequent cardiac-focused sequences [3]. These anatomical planes capture a broad thoracic region, encompassing multiple organs, and may reveal non-cardiac findings that can aid in diagnosing primary cardiac pathology or guiding appropriate patient management [4; 5].  
Extracardiac findings (ECFs) are defined as any non-cardiac abnormality, categorized by clinical relevance into major, significant, and insignificant findings, which may be incidentally detected in these preliminary sequences. Studies have shown that approximately 36 ECFs are identified per 100 CMR examinations, with nearly half being clinically meaningful [4; 6; 7; 8; 9]. Despite their potential significance, these preliminary images are often disregarded as they fall outside the primary scope of cardiac examinations. Moreover, the wide spectrum of potential abnormalities increases the likelihood of overlooking them. Thus, automated detection of ECFs could assist clinicians by highlighting suspected abnormalities, potentially prompting timely referrals to other specialties.  
To the best of the authors' knowledge, the study by [PERSON] et al. [5] stands as the only attempt to automate ECF detection in CMR HASTE images. Their work explored two deep-learning-based supervised approaches: binary classification at the image level and multi-label classification at the organ level. However, their results exhibited low sensitivity [5], falling short of acceptable values for clinical application. This shortfall can be attributed to inherent challenges in supervised methods, including their difficulty in handling small labeled datasets and inadequate outlier clustering capabilities.  
Conversely, anomaly detection (AD) methods shift the focus to the normal class, which is easier to model/cluster due to its relative uniformity and data abundance. With this methodology, anomalies can be identified simply as deviations [10; 11]. These methods have recently found utility in various domains, including brain MR [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22], thoracic X-ray [18; 23; 24], and industrial imaging [25; 26; 27; 28; 29; 30; 31; 32; 33], all of which involve images with lower structural complexity and variance. Since achieving satisfactory performance on such datasets does not guarantee favorable outcomes on datasets characterized by higher variance in normality, our study aims to investigate the effectiveness of recent AD methods in addressing a more complex task--detecting ECFs in CMR images. To achieve this, we conducted a comparative analysis of state-of-the-art unsupervised, semi-supervised, and open-set supervised AD methodologies and compared them with two supervised baselines.  
In summary, this work presents the following contributions:  
1. Systematic evaluation of AD methods for detecting ECFs in CMR images, considering an extensive set of approaches, including unsupervised, semi-supervised, and open-set supervised methodologies;
2. Comparison of these AD benchmarked methods with two fully supervised baselines, allowing a better perception of the effectiveness of these AD methods;
3. In-depth discussion of the strengths and weaknesses of AD methods in a challenging dataset, highlighting possible future directions.