  Automated organ at risk (OAR) segmentation is crucial for radiation therapy
planning in CT scans, but the generated contours by automated models can be
inaccurate, potentially leading to treatment planning issues. The reasons for
these inaccuracies could be varied, such as unclear organ boundaries or
inaccurate ground truth due to annotation errors. To improve the model's
performance, it is necessary to identify these failure cases during the
training process and to correct them with some potential post-processing
techniques. However, this process can be time-consuming, as traditionally it
requires manual inspection of the predicted output. This paper proposes a
method to automatically identify failure cases by setting a threshold for the
combination of Dice and Hausdorff distances. This approach reduces the
time-consuming task of visually inspecting predicted outputs, allowing for
faster identification of failure case candidates. The method was evaluated on
20 cases of six different organs in CT images from clinical expert curated
datasets. By setting the thresholds for the Dice and Hausdorff distances, the
study was able to differentiate between various states of failure cases and
evaluate over 12 cases visually. This thresholding approach could be extended
to other organs, leading to faster identification of failure cases and thereby
improving the quality of radiation therapy planning.

Image and Video Processing;Computer Vision and Pattern Recognition