Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2024-06-08
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
Series
Communications Medicine, Volume 4, issue 1, pp. 1-12
Abstract
Background Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical. Methods Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach. Results We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail. Conclusions Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.Description
The work of Joel Jaskari, Jaakko Sahlsten, and Kimmo K. Kaski was supported in part by the Academy of Finland under Project 345449. Antti Mäkitie is supported in part by a grant from the Finnish Society of Sciences and Letters. Kareem A. Wahid is supported by the Dr. John J. Kopchick Fellowship through The University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, the American Legion Auxiliary Fellowship in Cancer Research, and an NIH/National Institute for Dental and Craniofacial Research (NIDCR) F31 fellowship (1 F31DE031502-01) and the NCI NRSA Image Guided Cancer Therapy Training Program (T32CA261856). Benjamin H. Kann is supported by an NIH/National Institute for Dental and Craniofacial Research (NIDCR) K08 Grant (K08DE030216). Clifton D. Fuller receives related grant support from the NCI NRSA Image Guided Cancer Therapy Training Program (T32CA261856), as well as additional unrelated salary/effort support from NIH institutes. Dr. Fuller receives grant and infrastructure support from MD Anderson Cancer Center via: the Charles and Daneen Stiefel Center for Head and Neck Cancer Oropharyngeal Cancer Research Program; the Program in Image-guided Cancer Therapy; and the NIH/NCI Cancer Center Support Grant (CCSG) Radiation Oncology and Cancer Imaging Program (P30CA016672). Dr. Fuller has received unrelated direct industry grant/in-kind support, honoraria, and travel funding from Elekta AB.
Keywords
Other note
Citation
Sahlsten, J, Jaskari, J, Wahid, K A, Ahmed, S, Glerean, E, He, R, Kann, B H, Mäkitie, A, Fuller, C D, Naser, M A & Kaski, K 2024, ' Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning ', Communications Medicine, vol. 4, no. 1, 110, pp. 1-12 . https://doi.org/10.1038/s43856-024-00528-5
