Abstract
Connectomics aims to map the brain’s neural wiring by segmenting cellular structures from high-resolution electron microscopy (EM) images. Manual labeling and proofreading remain a major bottleneck for accurate extraction of microstructures. While computational models have advanced automated segmentation, they typically require training from scratch on each dataset, demanding substantial annotated data. Domain adaptation methods address this by transferring knowledge from a labeled source to a less-annotated target. However, existing approaches are limited to adaptation from a single source domain. This overlooks the potential benefits of integrating information from multiple diverse domains, motivating the development of multidomain adaptation. To address this, we propose MACS, the first known multi-domain adaptation framework that combines knowledge from multiple heterogeneous source domains to learn segmentation in the target domain, and employs active learning to efficiently select the most informative target samples for annotation. MACS uses information-theoretic weighting to combine source domains, and introduces a novel and efficient Bayesian Laplace approximation for uncertainty estimation. Our extensive experiments across nine connectomics datasets demonstrate that MACS consistently and substantially outperforms state-of-the-art models, even under limited annotation budgets, with a mean improvement of 5.89% at the lowest annotation budget and 27.72% at the highest annotation budget. In-depth analyses further reveal that MACS offers mechanistic interpretability by quantifying and explicitly upweighting the most transferable source domains for each target.
The preprocessed datasets and the source code of MACS are publicly available at http://github.com/abrarrahmanabir/MACS.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The preprocessed datasets and the source code of MACS are publicly available at http://github.com/abrarrahmanabir/MACS.
