ESTRO 2022 - Abstract Book

S39

Abstract book

ESTRO 2022

The corrective delineation procedure consisted of 3 annotation sessions, with models left training overnight, on the newly expanded dataset of annotations, between each session. The sessions included 20, 60, and 97 images, respectively. Delineation time was automatically recorded, including time to both review and correct identified mistakes.

Results

Figure 1: Delineation duration for each scan in order of completion time for both the auto-complete and full-correction methods. Auto-complete starts out slower than full-correction, but becomes significantly faster as more images are delineated. For comparison, manual liver delineation has been reported as taking between 4 and 8 minutes.

Figure 2: The mean dice with expert clinician delineations for the completed contours for both methods was similar to inter-annotator variation (~0.94), indicating high accuracy and suitability for clinical use. Conclusion During the first annotation session, the auto-complete method hampered contouring performance, as poor contour auto- completions increased the delineation workload but with sufficient training data and time, corrective annotation with auto- completion provided some reductions in contouring time, whilst maintaining high delineation accuracy. [1] https://arxiv.org/abs/2106.11942

PD-0066 Autocontouring of the mouse thorax using deep learning

J. Malimban 1 , D. Lathouwers 2 , H. Qian 3 , F. Verhaegen 4 , J. Wiedemann 1 , S. Brandenburg 1 , M. Staring 5

1 University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands; 2 Delft University of Technology, Department of Radiation Science and Technology, Delft, The Netherlands; 3 Amsterdam University Medical Centers (location AMC) and Cancer Center Amsterdam, Department of Medical Biology, Amsterdam, The Netherlands;