{Reference Type}: Journal Article
{Title}: A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer.
{Author}: Rasmussen ME;Nijkamp JA;Eriksen JG;Korreman SS;
{Journal}: Phys Imaging Radiat Oncol
{Volume}: 26
{Issue}: 0
{Year}: Apr 2023
暂无{DOI}: 10.1016/j.phro.2023.100426
{Abstract}: <B>UNASSIGNED: </B>Interactive segmentation seeks to incorporate human knowledge into segmentation models and thereby reducing the total amount of editing of auto-segmentations. By performing only interactions which provide new information, segmentation performance may increase cost-effectively. The aim of this study was to develop, evaluate and test feasibility of a deep learning-based single-cycle interactive segmentation model with the input being computer tomography (CT) and a small amount of information rich contours.<BR><B>UNASSIGNED: </B>A single-cycle interactive segmentation model, which took CT and the most cranial and caudal contour slices for each of 16 organs-at-risk for head-and-neck cancer as input, was developed. A CT-only model served as control. The models were evaluated with Dice similarity coefficient, Hausdorff Distance 95th percentile and average symmetric surface distance. A subset of 8 organs-at-risk were selected for a feasibility test. In this, a designated radiation oncologist used both single-cycle interactive segmentation and atlas-based auto-contouring for three cases. Contouring time and added path length were recorded.<BR><B>UNASSIGNED: </B>The medians of Dice coefficients increased with single-cycle interactive segmentation in the range of 0.004 (Brain)-0.90 (EyeBack_merged) when compared to CT-only. In the feasibility test, contouring time and added path length were reduced for all three cases as compared to editing atlas-based auto-segmentations.<BR><B>UNASSIGNED: </B>Single-cycle interactive segmentation improved segmentation metrics when compared to the CT-only model and was clinically feasible from a technical and usability point of view. The study suggests that it may be cost-effective to add a small amount of contouring input to deep learning-based segmentation models.