Multi-level distribution alignment-based domain adaptation for segmentation of 3D neuronal soma images

Deep learning networks are increasingly exploited in the field of neuronal soma segmentation. However, annotating dataset is also an expensive and time-consuming task. Unsupervised domain adaptation is an effective method to mitigate the problem, which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain. In this paper, we propose a multi-level distribution alignment-based unsupervised domain adaptation network (MDA-Net) for segmentation of 3D neuronal soma images. Distribution alignment is performed in both feature space and output space. In the feature space, features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and constrained to be domain invariant by adversarial adaptation strategy. In the output space, local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results. Then the distribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain, achieving refined segmentation performance of neuronal somata. Additionally, after a period of distribution alignment procedure, a portion of target samples with high confident pseudo-labels are selected as training data, which assist in learning a more adaptive segmentation network. We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.