bibtext: @InProceedings{ Nih_Estimation_MICCAI2024,
                   author = { Nihalaani, Rachaell and Kataria, Tushar and Adams, Jadie and Elhabian, Shireen Y. },
                   title = { { Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data. Slice propagation has recently emerged as an self-supervised approach that leverages slice registration as a self-supervised task to achieve full anatomy segmentation with minimal supervision. This approach significantly reduces the need for domain expertise, time and the cost associated with building fully annotated datasets required for training segmentation networks. However, this shift toward reduced supervision via deterministic networks raises concerns about the trustworthiness and reliability of predictions, especially when compared with more accurate supervised approaches. To address this concern, we propose the
  integration of calibrated uncertainty quantification (UQ) into slice propagation methods, providing insights into the model’s predictive reliability and confidence levels. Incorporating uncertainty measures enhances user confidence in self-supervised approaches, thereby improving their practical applicability. We conducted experiments on three datasets for 3D abdominal segmentation using five different UQ methods. The results illustrate that incorporating UQ not only improves model trustworthiness, but also segmentation accuracy. Furthermore, our analysis reveals various failure modes of slice propagation methods that might not be immediately apparent to end-users. This opens up new research avenues to improve the accuracy and trustworthiness of slice propagation methods.
title: Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation
authors: Nihalaani, Rachaell Kataria, Tushar Adams, Jadie Elhabian, Shireen Y.