## 4 Methods for Improved Reliability and Robustness
### Quality Control Techniques
#### 4.1.1 Pre-Analysis QC Tools  
Pre-analysis QC tools aim to identify erroneous inputs, addressing robustness by discarding them from the segmentation pipeline. The first barrier to overcome by this type of methods is to define quality itself. Some methods aim to detect predefined types of artifacts using learning-based approaches [73], heuristic techniques [71] or a combination of both [72, 75]. Other works, instead, follow a more qualitative definition that is based on a cardiologist's input [74,76,77]. In this category, machine learning classifiers provided with a set of qualitative labels (e.g., good/bad, discard/keep) are trained to emulate experts criteria, aiming to flag low quality. At inference, these models automatically retrieve the binary feedback, which replaces experts' decisions in high-throughput pipelines.  
In one of the first QC works, [PERSON] et al. [71] assess a perceptual difference model that quantitatively evaluates image quality of large volumes of magnetic resonance images to rate different image reconstruction algorithms. [PERSON] et al. [72] use box-, line-, histogram-, and texture-based features to train a random decision forest algorithm to distinguish between motion-corrupted and artifact-free images. [PERSON] et al. [73] aim to identify missing apical and/or basal LV slices in CMR images by using generative adversarial networks (GANs). This is achieved in two stages. First, adversarial examples are generated and exploited to extract high-level features from the CMR images. The features are then used to detect missing basal and apical slices. Such process improves not only robustness to adversarial examples, but also generalization performance for original examples. [PERSON] et al. [74] exploit different levels of k-space synthetic corruption to detect CMR images with low perceptual quality, defined as the mean of the individual ratings assigned by human observers. The authors use a data augmentation technique to handle the severe class imbalance between good-quality and motion-corrupted images, training two deep learning architectures to increase their robustness in the classification task. In [70,75], [PERSON] et al. present a quality control pipeline for CMR images in the UK Biobank dataset, capable of detecting three problematic scenarios to warn a human operator. The scenarios are low heart coverage, high inter-slice motion and low cardiac image contrast.  
Finally, some recent works have succeeded at integrating QC tools within a more complex cardiac analysis pipeline. [PERSON] et al. [76] use a ResNet [89] to classify CMR images as analyzable or non-analyzable. The network is trained with a dataset of 225 images labelled by an expert cardiologist. Those considered as analyzable move in forward in a cardiac analysis pipeline (see Section 4.1.2). [PERSON] et al. [77] present a DL-based pipeline for automated analysis of cardiac function. Inside the pipeline, two convolutional neural networks (CNNs) are trained to perform pre-analysis QC: a two-dimensional CNN with a recurrent long short-term memory layer for motion artifacts detection, and a two-dimensional CNN for detecting erroneous planning of the 4-chamber view. Flagged images are discarded from the subsequent segmentation step that serves as input to the cardiac function analysis.