  Training deep neural networks for 3D segmentation tasks can be challenging,
often requiring efficient and effective strategies to improve model
performance. In this study, we introduce a novel approach, DeCode, that
utilizes label-derived features for model conditioning to support the decoder
in the reconstruction process dynamically, aiming to enhance the efficiency of
the training process. DeCode focuses on improving 3D segmentation performance
through the incorporation of conditioning embedding with learned numerical
representation of 3D-label shape features. Specifically, we develop an
approach, where conditioning is applied during the training phase to guide the
network toward robust segmentation. When labels are not available during
inference, our model infers the necessary conditioning embedding directly from
the input data, thanks to a feed-forward network learned during the training
phase. This approach is tested using synthetic data and cone-beam computed
tomography (CBCT) images of teeth. For CBCT, three datasets are used: one
publicly available and two in-house. Our results show that DeCode significantly
outperforms traditional, unconditioned models in terms of generalization to
unseen data, achieving higher accuracy at a reduced computational cost. This
work represents the first of its kind to explore conditioning strategies in 3D
data segmentation, offering a novel and more efficient method for leveraging
annotated data. Our code, pre-trained models are publicly available at
https://github.com/SanoScience/DeCode .
