Uncertainty Quantification for Deep Learning-based Metastatic Tumor Delineation on ⁶⁸Ga-DOTATATE PET/CT Images

Deep learning (DL) based metastatic tumor delineation is an important tool for the automatic evaluation of advanced malignancies. Although recent studies have demonstrated high accuracy in this task, none have quantified the uncertainty of DL predictions, which is critical for clinical use. In this work, we compare DL uncertainty quantification (UQ) methods for metastatic tumor delineation.

A U-Net DL model was trained to delineate tumors on 59 ⁶⁸Ga-DOTATATE PET/CT images using 5-fold cross validation. Three UQ methods were implemented during model inference: Monte Carlo dropout (MCDO), test time augmentation (TTA), and deep ensembles (DE). UQ methods were compared using false positive (FP) filtering performance and correlations with delineation metrics, including Dice coefficient (DSC), cross entropy (CE), and biomarker extraction accuracy (uptake mean and total).

Each UQ method achieved statistical significance between true positive (TP) and FP predicted regions (p<0.001). AUCs±95%CI between FP and TP regions were 0.79±0.02, 0.81±0.02, and 0.80±0.02 for MCDO, TTA, and DE, respectively. Correlations between delineation metrics and uncertainty were ρ_DSC=-0.66, -0.73, -0.70; ρ_CE=0.87, 0.92, 0.93; ρ_mean=0.33, 0.34, 0.28; ρ_total=0.50, 0.57, 0.52 for MCDO, TTA and DE, respectively.

The TTA method demonstrated superior performance across the majority of UQ evaluations. Conveniently, this method also offers the advantage of lower computational demand compared to other methods.