Conditional diffusion model with spatial attention and latent embedding for medical image segmentation

TL;DR

This paper introduces cDAL, a conditional diffusion model with spatial attention and latent embedding, achieving faster and more accurate medical image segmentation with higher Dice scores and mIoU on multiple datasets.

Contribution

The paper presents a novel diffusion model incorporating spatial attention and latent embedding to improve speed and accuracy in medical image segmentation.

Findings

Significant improvement in Dice scores and mIoU over state-of-the-art methods.

Faster training and sampling due to reduced time-steps.

Effective segmentation of discriminative regions in medical images.

Abstract

Diffusion models have been used extensively for high quality image and video generation tasks. In this paper, we propose a novel conditional diffusion model with spatial attention and latent embedding (cDAL) for medical image segmentation. In cDAL, a convolutional neural network (CNN) based discriminator is used at every time-step of the diffusion process to distinguish between the generated labels and the real ones. A spatial attention map is computed based on the features learned by the discriminator to help cDAL generate more accurate segmentation of discriminative regions in an input image. Additionally, we incorporated a random latent embedding into each layer of our model to significantly reduce the number of training and sampling time-steps, thereby making it much faster than other diffusion models for image segmentation. We applied cDAL on 3 publicly available medical image segmentation datasets (MoNuSeg, Chest X-ray and Hippocampus) and observed significant qualitative and quantitative improvements with higher Dice scores and mIoU over the state-of-the-art algorithms. The source code is publicly available at https://github.com/Hejrati/cDAL/.