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MODEL-BASED LABEL-TO-IMAGE DIFFUSION FOR SEMI-SUPERVISED CHOROIDAL VESSEL SEGMENTATION

DOI:
10.60864/cgr5-bc52
Citation Author(s):
Submitted by:
kun huang
Last updated:
6 June 2024 - 10:54am
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
Kun Huang
Paper Code:
BISP-P8.10
 

Current successful choroidal vessel segmentation methods rely on large amounts of voxel-level annotations on the 3D optical coherence tomography images, which are hard and time-consuming. Semi-supervised learning solves this issue by enabling model learning from both unlabeled data and a limited amount of labeled data. A challenge is the defective pseudo labels generated for the unlabeled data. In this work, we propose a model-based label-to-image diffusion (MLD) framework for semi-supervised choroidal vessel segmentation. We first generate pseudo labels from unlabeled images with a coarse correspondence using a model-based strategy. Then, we generate precisely corresponding images of pseudo labels by a hierarchical diffusion probabilistic model. We evaluated our method on myopia data with a new topological connectivity metric. The quantitative and qualitative experimental results indicate the effectiveness of the label-to-image diffusion framework and its benefit for enhancing the existing supervised choroidal segmentation methods. The code is available at: https://github.com/nicetomeetu21/MLD.

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