mlsanityData to 7 Oct 2026

Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data

arXiv 2305.00950 ↗First posted 1 May 2023eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Uncertainty quantification in medical images has become an essential addition to segmentation models for practical application in the real world. Although there are valuable developments in accurate uncertainty quantification methods using 2D images and slices of 3D volumes, in clinical practice, the complete 3D volumes (such as CT and MRI scans) are used to evaluate and plan the medical procedure. As a result, the existing 2D methods miss the rich 3D spatial information when resolving the uncertainty. A popular approach for quantifying the ambiguity in the data is to learn a distribution over the possible hypotheses. In recent work, this ambiguity has been modeled to be strictly Gaussian. Normalizing Flows (NFs) are capable of modelling more complex distributions and thus, better fit the embedding space of the data. To this end, we have developed a 3D probabilistic segmentation framework augmented with NFs, to enable capturing the distributions of various complexity. To test the proposed approach, we evaluate the model on the LIDC-IDRI dataset for lung nodule segmentation and quantify the aleatoric uncertainty introduced by the multi-annotator setting and inherent ambiguity in the CT data. Following this approach, we are the first to present a 3D Squared Generalized Energy Distance (GED) of 0.401 and a high 0.468 Hungarian-matched 3D IoU. The obtained results reveal the value in capturing the 3D uncertainty, using a flexible posterior distribution augmented with a Normalizing Flow. Finally, we present the aleatoric uncertainty in a visual manner with the aim to provide clinicians with additional insight into data ambiguity and facilitating more informed decision-making.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 1 May 2023.

Builds on

10 of 15 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  3. 2015Variational Inference with Normalizing Flowscited by 2,161
  4. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  5. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  6. 2019Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishingcited by 191
  7. 2018Sylvester Normalizing Flows for Variational Inferencecited by 71
  8. 2019Supervised Uncertainty Quantification for Segmentation with Multiple Annotationscited by 42
  9. 2020Uncertainty quantification in medical image segmentation with normalizing flowscited by 23
  10. 2021Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flowscited by 13

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  2. 2024Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentationcited by 3
  3. 2023Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imagingcited by 2
  4. 2023Segmentation-based Assessment of Tumor-Vessel Involvement for Surgical Resectability Prediction of Pancreatic Ductal Adenocarcinomacited by 2