Update README.md

Author: WuJunde

README.md

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 # MedSegDiff: Medical Image Segmentation with Diffusion Model
 MedSegDiff a Diffusion Probabilistic Model (DPM) based framework for Medical Image Segmentation. The algorithm is elaborated on our paper [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://arxiv.org/abs/2211.00611) and [MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer](https://arxiv.org/pdf/2301.11798.pdf).
 
-<img align="left" width="170" height="170" src="https://github.com/WuJunde/MedSegDiff/blob/master/medsegdiff_showcase.gif"> Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. After training, we can use the Diffusion Model to generate data by simply passing randomly sampled noise through the learned denoising process.In this project, we extend this idea to medical image segmentation. We utilize the original image as a condition and generate multiple segmentation maps from random noises, then perform ensembling on them to obtain the final result. This approach captures the uncertainty in medical images and outperforms previous methods on several benchmarks.
 
 
 ## A Quick Overview