ESTRO 2022 - Abstract Book

S276

Abstract book

ESTRO 2022

Results The DSC for the prostate gland was 84% ± 3%, for the heart’s left ventricle it was 85% ± 2.5%, for the adventitia it was 85%±1%, and for the lumen 90% ± 2%. The average rand error index for all cases was less than 0.2. An example of the model performance for each clinical scenario is shown in Figure 2, including (A) the original image with the ground truth (orange) and the predicted (blue) contours, (B) the manual (ground truth) annotation masks from experts, (C) the segmentation mask derived from the model and (D) the heatmaps indicating how important are the pixels in the image that contribute to the segmentation result.

Conclusion A generic DL-based segmentation architecture is proposed with state-of-the-art performance. A module for model explainability was introduced aiming to improve the consistency and efficiency of the segmentation process by providing qualitative information on the model’s predictions, thereby promoting clinical acceptability. In the future, incorporating explainability measures in the entire treatment planning workflow, such as registration and dose prediction, will lead to a potential improvement in clinical practice and patient treatment.

PD-0315 Evaluation of three AI-based CT auto-contouring systems for head&neck, thorax and pelvis

M. Casati 1 , M. Loi 2 , C. Arilli 1 , L. Marrazzo 1 , C. Talamonti 1,3 , M. Zani 1 , A. Compagnucci 1 , G. Simontacchi 4 , V. Di Cataldo 5 , I. Desideri 3 , P. Bonomo 4 , N. Franza 6 , D. Raspanti 7 , R. Pellegrini 8 , L. Livi 3,2 , S. Pallotta 3,1 1 Azienda Ospedaliero Universitaria Careggi, Medical Physics, Florence, Italy; 2 Azienda Ospedaliero Universitaria Careggi, Radiotherapy Unit, Florence, Italy; 3 University of Florence, Department of Experimental and Clinical Biomedical Sciences, Florence, Italy; 4 Azienda Ospedaliero Universitaria Careggi, Radiation Oncology, Florence, Italy; 5 Florentine Institute of Care and Assistance (IFCA), Radiation Oncology, Florence, Italy; 6 DosimETrICA, DosimETrICA, Nocera Inferiore (SA), Italy; 7 Temasinergie S.p.A., Radiotherapy and Diagnostic Radiology, Faenza (RA), Italy; 8 Elekta AB, Global Clinical Science, Stockholm, Sweden Purpose or Objective To evaluate both performances and clinical acceptability of auto-contours generated by three AI-based software on 18 CT studies: 6 Head and Neck (H&N), 6 Thorax (T), and 6 Pelvis (P). Materials and Methods The structures listed in table 1 have been assessed, for each test study. The evaluated AI-contours were generated with deep learning algorithms by: Contour Protégé AI (Protégé) v. 2.0 (MIM software Inc. 7.1.5), Limbus Contour (Limbus) v.