Keywords: Diagnosis/Prediction, Brain
Motivation: Deep learning-based segmentation methods have shown promising results; however, they require a large number of segmentation labels for training, which is very costly to obtain, especially for 3D labels.
Goal(s): Our goal is to achieve promising 3D segmentation results with few labels by exploiting the ability to capture semantic information from 2D diffusion models trained without labels.
Approach: We train simple pixel classifiers using features extracted from 2D diffusion models that have been trained with slices from three orthogonal orientations.
Results: In our experiments on the Human Connectome Project database, our proposed method outperformed conventional segmentation methods in a few labeled scenarios.
Impact: Our proposed method for segmenting subcortical brain structures can be readily applied to pre-trained diffusion models with only a few labeled data, while also generating paired segmentation labels for the images produced by diffusion models.
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