# Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

arXiv: https://arxiv.org/abs/1805.02718 · HTML: https://mlsanity.com/paper/1805.02718/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 11 of 11)

- [NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale](https://mlsanity.com/paper/2107.05840/) (2021)
- [Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey](https://mlsanity.com/paper/2206.07171/) (2022)
- [CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy](https://mlsanity.com/paper/2101.04266/) (2021)
- [PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics](https://mlsanity.com/paper/2112.05754/) (2021)
- [Domain Adaptive Synapse Detection with Weak Point Annotations](https://mlsanity.com/paper/2308.16461/) (2023)
- [Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly](https://mlsanity.com/paper/2602.17997/) (2026)
- [Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentation](https://mlsanity.com/paper/2406.17936/) (2024)
- [ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading](https://mlsanity.com/paper/2606.21116/) (2026)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [Towards Generalized Synapse Detection Across Invertebrate Species](https://mlsanity.com/paper/2509.17041/) (2025)
- [Benchmarking Vision-Language Models on Synapse Detection and Proofreading in Connectomics](https://mlsanity.com/paper/2609.36492/) (2026)

## Abstract (arXiv)

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available.