Analysing Diffusion Segmentation for Medical Images

• URL: http://arxiv.org/abs/2403.14440v1
• Date: Thu, 21 Mar 2024 14:45:54 GMT
• Title: Analysing Diffusion Segmentation for Medical Images
• Authors: Mathias Öttl, Siyuan Mei, Frauke Wilm, Jana Steenpass, Matthias Rübner, Arndt Hartmann, Matthias Beckmann, Peter Fasching, Andreas Maier, Ramona Erber, Katharina Breininger,
• Abstract summary: We critically analyse and discuss how diffusion segmentation for medical images differs from diffusion image generation. We also conduct an assessment how proposed diffusion segmentation architectures perform when trained directly for segmentation.
• Score: 2.387226161755373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspired their adaptation for image segmentation, where multiple predictions of the model can produce segmentation results that not only achieve high quality but also capture the uncertainty inherent in the model. Here, powerful architectures were proposed for improving diffusion segmentation performance. However, there is a notable lack of analysis and discussions on the differences between diffusion segmentation and image generation, and thorough evaluations are missing that distinguish the improvements these architectures provide for segmentation in general from their benefit for diffusion segmentation specifically. In this work, we critically analyse and discuss how diffusion segmentation for medical images differs from diffusion image generation, with a particular focus on the training behavior. Furthermore, we conduct an assessment how proposed diffusion segmentation architectures perform when trained directly for segmentation. Lastly, we explore how different medical segmentation tasks influence the diffusion segmentation behavior and the diffusion process could be adapted accordingly. With these analyses, we aim to provide in-depth insights into the behavior of diffusion segmentation that allow for a better design and evaluation of diffusion segmentation methods in the future.

Related papers

• Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation [56.87049651707208]
  Few-shot Semantic has evolved into In-context tasks, morphing into a crucial element in assessing generalist segmentation models. Our initial focus lies in understanding how to facilitate interaction between the query image and the support image, resulting in the proposal of a KV fusion method within the self-attention framework. Based on our analysis, we establish a simple and effective framework named DiffewS, maximally retaining the original Latent Diffusion Model's generative framework.
  arXiv  Detail & Related papers  (2024-10-03T10:33:49Z)
• FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms [60.195642571004804]
  We propose FlowSDF, an image-guided conditional flow matching framework to represent the signed distance function (SDF) By learning a vector field that is directly related to the probability path of a conditional distribution of SDFs, we can accurately sample from the distribution of segmentation masks.
  arXiv  Detail & Related papers  (2024-05-28T11:47:12Z)
• Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation [15.275335829889086]
  We propose a conditional score-based generative modeling framework for medical image segmentation. We evaluate our method on the segmentation of the left ventricle from 65 transthoracic echocardiogram videos. Our proposed model not only outperformed the compared methods in terms of segmentation accuracy, but also showed potential in estimating segmentation uncertainties.
  arXiv  Detail & Related papers  (2023-12-19T22:50:02Z)
• Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models [70.3497683558609]
  A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. We propose a novel method for multi-expert prediction using diffusion models.
  arXiv  Detail & Related papers  (2023-06-15T10:01:05Z)
• Denoising Diffusion Semantic Segmentation with Mask Prior Modeling [61.73352242029671]
  We propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a denoising diffusion generative model. We evaluate the proposed prior modeling with several off-the-shelf segmentors, and our experimental results on ADE20K and Cityscapes demonstrate that our approach could achieve competitively quantitative performance.
  arXiv  Detail & Related papers  (2023-06-02T17:47:01Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  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 sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation [19.036821997968552]
  We propose a conditional Bernoulli Diffusion model for medical image segmentation (BerDiff) Our results show that our BerDiff outperforms other recently published state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-10T07:21:38Z)
• Diffusion Models for Implicit Image Segmentation Ensembles [1.444701913511243]
  We present a novel semantic segmentation method based on diffusion models. By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images. Compared to state-of-the-art segmentation models, our approach yields good segmentation results and, additionally, meaningful uncertainty maps.
  arXiv  Detail & Related papers  (2021-12-06T16:28:15Z)
• Label-Efficient Semantic Segmentation with Diffusion Models [27.01899943738203]
  We demonstrate that diffusion models can also serve as an instrument for semantic segmentation. In particular, for several pretrained diffusion models, we investigate the intermediate activations from the networks that perform the Markov step of the reverse diffusion process. We show that these activations effectively capture the semantic information from an input image and appear to be excellent pixel-level representations for the segmentation problem.
  arXiv  Detail & Related papers  (2021-12-06T15:55:30Z)
• SegDiff: Image Segmentation with Diffusion Probabilistic Models [81.16986859755038]
  Diffusion Probabilistic Methods are employed for state-of-the-art image generation. We present a method for extending such models for performing image segmentation. The method learns end-to-end, without relying on a pre-trained backbone.
  arXiv  Detail & Related papers  (2021-12-01T10:17:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.