An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation within a multicenter lung cancer cohort

Agon Olloni*, Ebbe Laugaard Lorenzen, Stefan Starup Jeppesen, Axel Diederichsen, Robert Finnegan, Lone Hoffmann, Charlotte Kristiansen, Marianne Knap, Marie Louise Holm Milo, Ditte Sloth Møller, Mette Pøhl, Gitte Persson, Hella Mb Sand, Nis Sarup, Rune Thing, Carsten Brink, Tine Schytte

*Corresponding author af dette arbejde

Abstract

BACKGROUND AND PURPOSE: Irradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies.

MATERIALS AND METHODS: The hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set.

RESULTS: The hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures.

CONCLUSION: The hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.

OriginalsprogEngelsk
Artikelnummer110065
TidsskriftRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Vol/bind191
ISSN0167-8140
DOI
StatusUdgivet - feb. 2024

Fingeraftryk

Dyk ned i forskningsemnerne om 'An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation within a multicenter lung cancer cohort'. Sammen danner de et unikt fingeraftryk.

Citationsformater