MExConn: A Mechanistically Interpretable Multi-Expert Framework for Multi-Organelle Segmentation in Connectomics

Abstract Electron microscopy (EM) provides subcellular resolution which has made it a critical tool in fields such as cellular biology and connectomics. However, manual annotation of subcellular organelles in these EM images is extremely labor-intensive and impractical at scale. While computational segmentation methods have been developed, most existing approaches are limited to segmenting a single organelle at a time, neglecting the inherent shared information present in EM images containing multiple organelles. To address this, we present MExConn, the first known interpretable multi-expert U-Net architecture in the connectomics field that employs a shared encoder and multiple decoder heads to simultaneously segment multiple organelles from the same input EM image. MExConn significantly outperforms five baselines, including single-organelle model and four state-of-the-art connectomics segmentation models in all evaluation metrics, reducing the Variation of Information by up to 33.54% on average across organelles. A key novelty of our approach is that MExConn offers mechanistic interpretability by revealing that the shared encoder learns shared representations essential for accurately segmenting multiple organelles. Through systematic analysis of encoder gradients with respect to each decoder output, we identify channel-wise importance profiles and reveal that many encoder channels are jointly essential for all organelles, while others are organelle-specific. Rigorous experiments on three connectomics datasets demonstrate the effectiveness of MExConn in both segmentation performance and interpretability, establishing it as a principled approach for multi-organelle analysis in connectomics. The source code is publicly available at https://github.com/abrarrahmanabir/MExConn. Competing Interest Statement The authors have declared no competing interest.