## 4 Methods for Improved Reliability and Robustness
### Quality Control Techniques
#### 4.1.2 Post-Analysis QC Tools  
Post-analysis QC tools focus on the assessment of the segmentation outputs of a model. In this sense, we consider these tools as targeting reliability, as the quality of the segmentation output is the final indicator of the model's performance. Methods under this category follow two main approaches to performance assessment. They act either as binary classifiers, assigning correct/incorrect labels to a segmentation, or as regressors, which attempt to infer well-known validation metrics, such as the Dice Score or the Hausdorff Distance (HD), or uncertainty estimates. Among regressors, [PERSON] et al. [82] train an SVM regressor from DSCs measured against ground truth to build confidence measures and rank candidate segmentation models against each other. [PERSON] et al. [83] propose the Reverse Classification Accuracy (RCA), a registration-based method relying on the spatial overlap between predicted segmentations and reference atlases as a pseudo-measure of the performance of a segmentation model on new data. The technique has been extensively validated in the UK Biobank [84], despite being computationally expensive at inference time or prone to failure at the registration stage [90]. [PERSON] et al. [85] rely on a CNN to predict the DSC of unseen segmented data. The authors are the first to observe that it is difficult to obtain a balanced set of labelled data reflecting the complete feasible distribution of DSCs. [PERSON] et al. [86] use an ensemble of neural networks to segment the LV from T1 magnetic resonance, while providing an estimate of the DSC of the predicted segmentation using multiple linear regression. [PERSON] et al. [87] question the usefulness of 3D DSCs as the sole measure of segmentation quality, as it excludes specific information related to the single slices, which is actually fundamental when analysing the base and the apex. The authors overcome this limitation by performing simultaneously quality control at 2D-level and 3D-level using a CNN capable of predicting both 3D and 2D DSCs. [PERSON] and [PERSON] [88] use a convolutional autoencoder that reconstructs input segmentation masks into pseudo ground truth masks. Pseudo DSC and HD are then measured between the segmentations and their reconstructions that act as surrogate measures of the quality of the segmentation results. Among the classifiers, [PERSON] et al. [78] use statistical, pattern and fractal descriptors in a random forest classifier, which detect segmentation failures to be corrected or removed from subsequent analyses. [PERSON] et al. [79] use the uncertainty information captured in the evidence lower bound (ELBO) produced by a Bayesian CNN to identify incorrect segmentations, which can be rejected or flagged for revision by an expert. In [80], segmentation uncertainty is first assessed at the voxel level by using the multi-class entropy and Monte Carlo dropout. After deriving uncertainty maps, a CNN is trained to detect image regions containing local segmentation failures that potentially need correction by an expert. The authors differentiate tolerated errors, which lay within the range of inter-observer variability, and the segmentation failures, which are flagged to be corrected by an expert. [PERSON] et al. [81] propose combining self-supervision loss terms and post hoc uncertainty estimations into a reliable and lightweight novelty score that allows anomalous samples' identification. The RCA [83], a regressor approach, has been embedded into the method proposed in [76], where the authors build a cardiac analysis pipeline that integrates both pre- (see Section 4.1.1) and post-analysis QC. For the latter, they estimate several quality metrics between pairs of segmentations, before and after being processed by RCA. Based on these values, an SVM binary classifier is trained to discriminate between poor and good quality segmentations. As [76], [PERSON] et al. [77] integrate pre- and post-analysis QC in a unified end-to-end pipeline.