Keywords: Diffusion models, segmentation, uncertainty estimation
TL;DR: We apply diffusion models on a segmentation task, which allows us to compute an implicit ensemble of segmentations and pixel-wise uncertainty maps.
Abstract: Diffusion models have shown impressive performance for generative modelling of images. In this paper, 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. To generate an image-specific segmentation, we train the model on the ground truth segmentation, and use the image as a prior during training and in every step during the sampling process. With the given stochastic sampling process, we can generate a distribution of segmentation masks. This property allows us to compute pixel-wise uncertainty maps of the segmentation, and allows an implicit ensemble of segmentations that increases the segmentation performance. We evaluate our method on the BRATS2020 dataset for brain tumor segmentation. Compared to state-of-the-art segmentation models, our approach yields good segmentation results and, additionally, detailed uncertainty maps.
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Paper Type: both
Primary Subject Area: Segmentation
Secondary Subject Area: Uncertainty Estimation
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Code And Data: The code can be found at https://github.com/JuliaWolleb/Diffusion-based-Segmentation. The data can be downloaded at https://www.med.upenn.edu/cbica/brats2020/data.html.
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 3 code implementations](https://www.catalyzex.com/paper/arxiv:2112.03145/code)