Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT

Schott, Brayden (author), Santoro Fernandes, Victor (author), Klaneček, Žan (author), Perlman, Scott (author), Jeraj, Robert (author)

Objective. Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncertainty quantification (UQ) methods exist to capture model uncertainty. Yet, it is not clear which method is optimal for a given task. The purpose of this work was to investigate several commonly used UQ methods for the critical yet understudied task of metastatic lesion segmentation on whole body PET/CT. Approach. 59 whole body $^{68}$Ga-DOTATATE PET/CT images of patients undergoing theranostic treatment of metastatic neuroendocrine tumors were used in this work. A 3D U-Net was trained for lesion segmentation following five-fold cross validation. Uncertainty measures derived from four UQ methods—probability entropy, Monte Carlo dropout, deep ensembles, and test time augmentation—were investigated. Each uncertainty measure was assessed across four quantitative evaluations: (1) its ability to detect artificially degraded image data at low, medium, and high degradation magnitudes; (2) to detect false-positive (FP) predicted regions; (3) to recover false-negative (FN) predicted regions; and (4) to establish correlations with model biomarker extraction and segmentation performance metrics. Main results. Test time augmentation and probability entropy respectively achieved the highest and lowest degraded image detection at low (AUC = 0.54 vs. 0.68), medium (AUC = 0.70 vs. 0.82), and high (AUC = 0.83 vs. 0.90) degradation magnitudes. For detecting FPs, all UQ methods achieve strong performance, with AUC values ranging narrowly between 0.77 and 0.81. FN region recovery performance was strongest for test time augmentation and weakest for probability entropy. Performance for the correlation analysis was mixed, where the strongest performance was achieved by test time augmentation for SUV$_{\mathrm{total}}$ capture ($\rho$ = 0.57) and segmentation Dice coefficient ($\rho$ = 0.72), by Monte Carlo dropout for SUV$_{\mathrm{mean}}$ capture ($\rho$ = 0.35), and by probability entropy for segmentation cross entropy ( = 0.96). Significance. Overall, test time augmentation demonstrated superior UQ performance and is recommended for use in metastatic lesion segmentation task. It also offers the advantage of being post hoc and computationally efficient. In contrast, probability entropy performed the worst, highlighting the need for advanced UQ approaches for this task.

• Language: English
• Keywords: medical physics, medical imaging, tumors, deep learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 17 str.
• Numbering: Vol. 70, no. 11, art. no. 115009
• PID: 20.500.12556/RUL-169797
• UDC: 616-073:53
• ISSN on article: 0031-9155
• DOI: 10.1088/1361-6560/add9df
• COBISS.SI-ID: 239055363
• Publication date in RUL: 11.06.2025

Record is a part of a journal

• Title: Physics in medicine & biology
• Shortened title: Phys. med. biol.
• Publisher: IOP Publishing, Institute of Physics and Engineering in Medicine
• ISSN: 0031-9155
• COBISS.SI-ID: 26128896

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: medicinska fizika, medicinsko slikanje, tumorji, globoko učenje