Conference Paper/Proceeding/Abstract 690 views 185 downloads
Guided latent diffusion for universal medical image segmentation
International Conference on AI-Generated Content (AIGC 2024), Volume: 13649, Start page: 7
Swansea University Authors:
Chen Hu, Hanchi Ren, Xianghua Xie
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DOI (Published version): 10.1117/12.3065214
Abstract
Deep learning based medical segmentation still presents a great challenge due to the lack of large-scale datasets in the medical domain. The existing publicly available datasets vary significantly in terms of imaging modalities and target anatomies. This paper presents a novel guided latent diffusio...
| Published in: | International Conference on AI-Generated Content (AIGC 2024) |
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| ISBN: | 9781510692114 9781510692121 |
| ISSN: | 0277-786X 1996-756X |
| Published: |
SPIE
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68384 |
| Abstract: |
Deep learning based medical segmentation still presents a great challenge due to the lack of large-scale datasets in the medical domain. The existing publicly available datasets vary significantly in terms of imaging modalities and target anatomies. This paper presents a novel guided latent diffusion model for universal medical segmentation, capable of segmenting diverse anatomical structures using a single and unified architecture. Given a Contrastive Language-Image Pretraining (CLIP) embedding specifying the target anatomical structure, the proposed model leverages a collection of datasets covering the diverse structures which can segment any anatomical targets that are presented in the aggregated data. By performing diffusion fully in latent space, we achieve comparable results to pixel-space diffusion with significantly lower computational cost. The proposed methods demonstrates competitive performance against existing deep learning-based discriminative approaches on several benchmarks. Furthermore, it shows strong generalization capabilities on unseen datasets. |
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| Keywords: |
Image segmentation; Data modeling; Diffusion; Performance modeling; Education and training; Anatomy; Visual process modeling; 3D modeling; Medical imaging; Denoising |
| College: |
Faculty of Science and Engineering |
| Start Page: |
7 |