# Volumetric Directional Diffusion: Anchoring Uncertainty Quantification in Anatomical Consensus for Ambiguous Medical Image Segmentation

arXiv: https://arxiv.org/abs/2603.04024 · HTML: https://mlsanity.com/paper/2603.04024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [SegDiff: Image Segmentation with Diffusion Probabilistic Models](https://mlsanity.com/paper/2112.00390/) (2021)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [PHiSeg: Capturing Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/1906.04045/) (2019)
- [The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT](https://mlsanity.com/paper/2307.01984/) (2023)
- [Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty](https://mlsanity.com/paper/2006.06015/) (2020)
- [Ambiguous Medical Image Segmentation using Diffusion Models](https://mlsanity.com/paper/2304.04745/) (2023)
- [WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis](https://mlsanity.com/paper/2402.19043/) (2024)
- [Stochastic Segmentation with Conditional Categorical Diffusion Models](https://mlsanity.com/paper/2303.08888/) (2023)
- [BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.04429/) (2023)
- [Hallucination Index: An Image Quality Metric for Generative Reconstruction Models](https://mlsanity.com/paper/2407.12780/) (2024)
- [Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model](https://mlsanity.com/paper/2407.05059/) (2024)
- [HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation](https://mlsanity.com/paper/2407.03548/) (2024)
- [Semantic Context Matters: Improving Conditioning for Autoregressive Models](https://mlsanity.com/paper/2511.14063/) (2025)
- [Step-Calibrated Diffusion for Biomedical Optical Image Restoration](https://mlsanity.com/paper/2403.13680/) (2024)
- [Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching](https://mlsanity.com/paper/2507.22418/) (2025)
- [Ambiguous Medical Image Segmentation Using Diffusion Schr"odinger Bridge](https://mlsanity.com/paper/2509.17187/) (2025)
- [Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction](https://mlsanity.com/paper/2502.20784/) (2025)
- [Model-Agnostic Gender Bias Control for Text-to-Image Generation via Sparse Autoencoder](https://mlsanity.com/paper/2507.20973/) (2025)
- [DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model](https://mlsanity.com/paper/2507.13087/) (2025)
- [FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis](https://mlsanity.com/paper/2511.08887/) (2025)

## Abstract (arXiv)

Ambiguous 3D medical image segmentation often involves boundaries where different expert delineations are non-identical yet clinically plausible. Modeling such inter-observer variability requires a careful balance between diversity and anatomical fidelity: deterministic models preserve coherent volumetric structures but collapse expert disagreement into a single mask, while stochastic generative models can produce diverse samples but may introduce disconnected components or slice-to-slice inconsistency when generating full 3D masks from unstructured noise. We propose Volumetric Directional Diffusion (VDD), a prior-anchored diffusion framework that shifts stochastic generation from full-mask synthesis to residual boundary exploration. VDD uses a coarse consensus prediction as an anatomical anchor and learns a directional diffusion process to generate plausible boundary variations around ambiguous regions while preserving stable volumetric topology. Experiments on three multi-rater datasets, including LIDC-IDRI, KiTS21, and ISBI 2015, show that VDD improves uncertainty distribution alignment while maintaining competitive segmentation accuracy and 3D structural consistency. These results suggest that prior-anchored residual diffusion can model clinically plausible expert disagreement without sacrificing anatomical fidelity.