UniSPAC: A Unified Segmentation Framework for Proofreading and Annotation in Connectomics
Bibliographic record
Abstract
Reconstructing dense neuronal connections from volume electron microscopy (vEM) images is a critical challenge in neuroscience, driving the development of various automatic neuron segmentation methods. Although current state-of-the-art automated segmentation methods can achieve high segmentation accuracy, they still require substantial manual proofreading and rely heavily on labeled datasets, which are often scarce, particularly for non-model organisms. Here, we introduce a Unified Segmentation framework for Proofreading and Annotation in Connectomics (UniSPAC) by providing the interactive segmentation model in 2D-level and the neuron tracing model in 3D-level. UniSPAC-2D allows users to correct its segmentation errors through point-based prompts, combining segmentation and proofreading in a single framework. UniSPAC-3D automatically traces neurons segmented by UniSPAC-2D across image slices, significantly reducing human involvement. Furthermore, UniSPAC-2D and UniSPAC-3D models can facilitate the semi-automatic generation of labeled data for new species, eliminating the need for external annotation tools. The fresh annotated data generated during proofreading in turn optimizes the interactive model through an online learning strategy, reducing the labeling effort for novel species over time. UniSPAC outperforms the start-of-the-art Segment Anything Model (SAM) in Drosophila segmentation, achieving 47x higher efficiency, and surpasses ACRLSD in cross-species segmentation on zebra finch data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".