# Normalizing Flows for Out-of-Distribution Detection: Application to Coronary Artery Segmentation
## 4 Conclusions  
While the performance of AI based methods has improved markedly over the past few years, semi-automated approaches are currently still being employed. One potential approach for significantly reducing the processing time is to pre-select regions of interest which are likely to require manual inspection and editing. Herein, we linked this pre-selection step to the topic of confidence and out-of-distribution detection, based on NF. The usage of a novel coupling layer which exhibits an inductive bias favoring the exploitation of semantical features instead of local pixel correlations was investigated on the task of detecting mismatched pairs of CCTA lumen images and their corresponding lumen segmentations. A network architecture employing such layers was tested against a Glow-like baseline. The proposed network showed better performance in OoD detection when tested against synthetic perturbations, while the sensitivity of detecting faulty annotations was close to inter-expert agreement. Samples from the model confirm that the learnt probability density managed to capture the relevant informational content from the training samples, instead of just modelling plain textures. Figure 16: Generated samples are not coherent across the entire 32 \(\times\) 32 spatial resolution, but only on the 16 \(\times\) 16 quadrants. Figure 15: Baseline model (**first row**) compared to proposed network (**last row**): AuRoC values for separating OoD mask-translated samples, using either top-features or bottom-features. Dashed black lines represent default model performance using regular log-probs. During model development and testing stages, only manual annotations were employed in the input mask-channel; therefore, the use of the investigated models is not tied to any specific segmentation model. Thus, any model-specific segmentation artefacts, which could possibly alter the observed probability density of correct image-mask pairs, are avoided by learning only from manual annotations (e.g., possible segmentation failure modes are not included in the learnt probability density). In deployment scenarios, the mask channel would be fed by a separate segmentation model and the proposed NF can therefore act as an independent audit model, detecting cases where the mask is not in full agreement with the underlying lumen images. As the failure modes of the NF model would be uncorrelated with the failure modes of the segmentation model, the proposed setup is better suited for robust pre-selection of vessel locations which are likely to require inspection and editing, leading to time savings when performing semi-automated CCTA lumen analysis. CCTA is a powerful non-invasive test for ruling out CAD, i.e., avoiding unnecessary invasive coronary angiography (ICA). A recent review has summarized the latest aspects addressing the CCTA suitability for selecting patients for invasive coronary angiography (ICA) and subsequent revascularization [29]. Clinical trials have shown that performing CCTA in patients receiving a clinical indication for ICA results in lower costs and more effective patient care [30]. However, small errors in the CCTA interpretation (e.g., minimal lumen area or diameter) can have a significant influence on the interpretation of the anatomical and/or functional significance of a stenosis. A large grey zone of uncertainty in the clinical interpretation may be the consequence. The method proposed herein allows for more confident decision making using CCTA imaging alone. Using the proposed out-of-domain detection method, the gray zone in the clinical interpretation can potentially be narrowed down. C.F.C. : Methodology, Software, Validation, Formal analysis, Writing--Original Draft, Visualization. L.M.I. : Conceptualization, Methodology, Writing--Original Draft, Writing--Review & Editing, Visualization, Project administration, Funding acquisition. S.C.: Conceptualization, Investigation, Resources, Data Curation. C.S. and M.W. : Conceptualization, Writing--Review and Editing, Supervision. P.F., S.S., F.A. and S.J.B.