A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer

Mathis Ersted Rasmussen; Jasper Albertus Nijkamp; Jesper Grau Eriksen; Stine Sofia Korreman

doi:10.1016/j.phro.2023.100426

A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer

Phys Imaging Radiat Oncol. 2023 Mar 5:26:100426. doi: 10.1016/j.phro.2023.100426. eCollection 2023 Apr.

Authors

Mathis Ersted Rasmussen^{1

2

3}, Jasper Albertus Nijkamp^{2

4}, Jesper Grau Eriksen^{1

3}, Stine Sofia Korreman^{2

3

4}

Affiliations

¹ Department of Experimental Oncology, Aarhus University Hospital, Denmark.
² Department of Clinical Medicine, Aarhus University, Denmark.
³ Department of Oncology, Aarhus University Hospital, Denmark.
⁴ Danish Center for Particle Therapy, Aarhus University Hospital, Denmark.

Abstract

Background and purpose: 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.

Methods and materials: 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.

Results: 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.

Conclusion: 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.

Keywords: Auto-contouring; Auto-segmentation; Cancer; Deep learning; Head-and-neck; Interactive segmentation; Organs-at-risk; Radiotherapy; Single-cycle; Treatment planning.