Ambiguous Medical Image Segmentation using Diffusion Models

2023/04/10 by Aimon Rahman, Jeya Maria Jose Valanarasu, Rahman, Aimon +5 · 14 citations
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #MRI in cancer diagnosis #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2304.04745

openalex publication_date 2023/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights.

Cited by

• Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
• StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space
• LatentFM: A Latent Flow Matching Approach for Generative Medical Image Segmentation
• Probabilistic Modeling of Multi-rater Medical Image Segmentation for Diversity and Personalization
• ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion
• BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
• TA-LSDiff:Topology-Aware Diffusion Guided by a Level Set Energy for Pancreas Segmentation
• Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation
• Ambiguous Medical Image Segmentation Using Diffusion Schrödinger Bridge
• FS-Diff: Semantic guidance and clarity-aware simultaneous multimodal image fusion and super-resolution
• DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
• Generic Event Boundary Detection via Denoising Diffusion
• Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation
• Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching

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• Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models
• Diffusion Models for Implicit Image Segmentation Ensembles
• Analysing Diffusion Segmentation for Medical Images
• DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model