Label fusion and training methods for reliable representation of inter-rater uncertainty

TL;DR

This paper compares label fusion methods for training deep learning models on inter-rater annotated medical data, demonstrating that SoftSeg frameworks improve calibration and variability preservation over conventional methods.

Contribution

It introduces a comprehensive comparison of three label fusion techniques within SoftSeg and conventional frameworks, highlighting SoftSeg's advantages in calibration and uncertainty representation.

Findings

SoftSeg models outperform conventional models in calibration.

Averaging labels with SoftSeg leads to better uncertainty estimation.

Best label fusion method varies by dataset.

Abstract

Medical tasks are prone to inter-rater variability due to multiple factors such as image quality, professional experience and training, or guideline clarity. Training deep learning networks with annotations from multiple raters is a common practice that mitigates the model's bias towards a single expert. Reliable models generating calibrated outputs and reflecting the inter-rater disagreement are key to the integration of artificial intelligence in clinical practice. Various methods exist to take into account different expert labels. We focus on comparing three label fusion methods: STAPLE, average of the rater's segmentation, and random sampling of each rater's segmentation during training. Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression. Our results, across 10 data splittings on two public datasets, indicate that SoftSeg models, regardless of the ground truth fusion method, had better calibration and preservation of the inter-rater rater variability compared with their conventional counterparts without impacting the segmentation performance. Conventional models, i.e., trained with a Dice loss, with binary inputs, and sigmoid/softmax final activate, were overconfident and underestimated the uncertainty associated with inter-rater variability. Conversely, fusing labels by averaging with the SoftSeg framework led to underconfident outputs and overestimation of the rater disagreement. In terms of segmentation performance, the best label fusion method was different for the two datasets studied, indicating this parameter might be task-dependent. However, SoftSeg had segmentation performance systematically superior or equal to the conventionally trained models and had the best calibration and preservation of the inter-rater variability.