| --- |
| license: other |
| pretty_name: ConnectomeBench2 |
| tags: |
| - connectomics |
| - proofreading |
| - 3d |
| - electron-microscopy |
| - mesh |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train/train-*.parquet |
| - split: validation |
| path: val/val-*.parquet |
| - split: test |
| path: test/test-*.parquet |
| --- |
| |
| # ConnectomeBench2 |
|
|
| ConnectomeBench2 is a unified benchmark for **automated proofreading of connectomic neural-segmentation data**. **401,170 samples** across 4 species (mouse, fly, human, zebrafish) and 5 sample types (real merge edits, real split edits, synthetic adjacent / junction / synapse controls), with the associated mesh geometry and electron-microscopy (EM) renderings. |
|
|
| Downstream trainers should treat this dataset as the single source of truth for sample identity, labels, train/validation/test split, and which task(s) a row is valid for. |
|
|
| ## Context: Connectomic Proofreading |
|
|
| **Connectomics** scans and automatically segments neurons to create large-scale brain maps at cellular resolution. Two types of **segmentation errors** can occur in this process, which need to be corrected (= **proofreading**): |
|
|
| - **False Splits** — corrected via merge corrections |
| - **False Merges** — corrected via split corrections |
|
|
| *Merge corrections* (of false splits) are applied to *multiple segments* that need to be correctly merged together. *Split corrections* (of false merges) are applied to *single segments* that need to be correctly split apart. |
|
|
| For this reason, this dataset contains renderings of both *single-segment* (pre-split or post-merge) and *dual-segment* (post-split or pre-merge) mesh geometry, where possible. EM data is provided in dual format only — segmentation on imaging level is contiguous, so the single-version can be derived from the union of the dual. |
|
|
| ## Renderings (geometry and EM imaging data) |
|
|
|  |
|
|
| (top: synapse merge-pair — both masks populated; bottom: junction control — single-mask only, mask B / seg B empty) |
|
|
| **Geometry files** (the `geometry` and `geometry_single` columns) are compressed `.npz` payloads that decode to `(3, 7, 224, 224) float16` arrays — three 2D views (front, side, top) × seven channels: |
|
|
| | ch | content | |
| |---|---| |
| | 0 | silhouette | |
| | 1 | depth | |
| | 2 | normal_x | |
| | 3 | normal_y | |
| | 4 | normal_z | |
| | 5 | mask A | |
| | 6 | mask B (empty in single-segment renders) | |
| |
| Note that single and dual segment renders differ not only in mask channels, but also subtly differ in all other channels, due to slight differences in mesh geometry from merging/splitting. |
| |
| **Free split-mask labels.** For `split_edit` rows, the dual-segment render (post-split) provides ground-truth split-mask labels (Mask A / Mask B channels) for the corresponding single-segment render (pre-split) — split-mask-generation tasks get pixel-level supervision without extra labeling. |
|
|
| **EM coverage.** EM views are not present on every sample. Coverage by `sample_type` (full dataset): |
|
|
| | sample_type | rows | has_em | |
| |---|---|---| |
| | adjacent_control | 121,333 | 100% | |
| | junction_control | 38,272 | 100% | |
| | synapse_control | 18,182 | 100% | |
| | merge_edit | 146,461 | 38% | |
| | split_edit | 77,213 | 23% | |
| | **total** | **401,170** | **63% (37% null)** | |
| |
| real human edits (merge_edit, split_edit) only got EM rendered on a stratified subset; synthetic controls all have EM. Filter by `has_em` if your task requires it. |
|
|
| **EM imaging files** (`em_xy` / `em_xz` / `em_yz` / `em_best` columns) are PNG-encoded 3-channel slices: |
|
|
| | ch | content | |
| |---|---| |
| | 0 | raw EM intensity | |
| | 1 | segment A mask | |
| | 2 | segment B mask | |
|
|
| Four imaging views per sample: three cardinal slices (xy, xz, yz) + a `best` slice at an oblique angle that maximizes the visible area of both segments (sum of their logs). |
|
|
| For single-segment tasks, segment A and B should be merged (and B zeroed). The `best` view may leak some dual-label information (it takes both labels into account); we advise against testing single-segment tasks on `em_best`. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("jeffbbrown2/connectomebench2-smoke", split="train") |
| sample = ds[0] |
| # sample["em_xy"] is a PIL Image (HF auto-decodes) |
| # sample["geometry"] is bytes — decode with: |
| import io, numpy as np |
| geom = np.load(io.BytesIO(sample["geometry"]))["arr_0"] # shape (3, 7, 224, 224) float16 |
| ``` |
|
|
| Or with raw `pyarrow`: |
|
|
| ```python |
| import pyarrow.parquet as pq |
| import numpy as np, io |
| df = pq.read_table("train/train-00000.parquet").to_pandas() |
| geom = np.load(io.BytesIO(df.iloc[0]["geometry"]))["arr_0"] |
| ``` |
|
|
| The `metadata/{train,val,test}.parquet` sidecars contain identifier/label/modality columns only (no image bytes) — useful for fast filtering or inspection. |
|
|
| ## Columns |
|
|
| ### Identifiers |
| - **`combined_sample_hash`** — primary key (md5 hex 32-char of `f"{source_archive}|{source_archive_sample_hash}"`); guaranteed unique across the dataset. |
| - **`source_archive_sample_hash`** — legacy 12-char hex hash from upstream; kept for traceability, not unique alone. |
| - **`source_archive`** — name of the originating render archive (e.g. `edits_and_adj_controls_fly`, `junction_controls_mouse`, `synapse_controls_fly`). 10 distinct values (5 archives × species). |
|
|
| ### Sample identity |
| - **`sample_type: str`** — single source of truth for what kind of sample this row is. Five values: |
| - `merge_edit` — positive merge-correction edit |
| - `split_edit` — positive split-correction edit |
| - `adjacent_control` — synthetic negative for merge-correction (segments adjacent to genuine correction) |
| - `junction_control` — putative junction in proofread neuron (negative merge-error-id sample) |
| - `synapse_control` — synapse pair across neurons (negative merge-correction) |
| - **`same_neuron: bool`** — derived from sample_type: |
| - `True` for `merge_edit`, `junction_control` |
| - `False` for `split_edit`, `adjacent_control`, `synapse_control` |
| - **`species: str`** — `fly` / `mouse` / `human` / `zebrafish`. |
|
|
| ### Image content |
| - **`geometry`** — bytes; compressed npz (key `"arr_0"`) decoding to `(3, 7, 224, 224) float16`. Null when the sample has no dual-segment render. |
| - **`geometry_single`** — same shape/dtype, single-segment version. Null when not present. |
| - **`em_xy` / `em_xz` / `em_yz` / `em_best`** — PIL Images (3-channel PNG, `(224, 224, 3) uint8`). Null when the row has no EM views. |
| - **`has_single_mask: bool`** — convenience flag. |
| - **`has_dual_mask: bool`** — convenience flag. |
| - **`has_em: bool`** — true if any `em_*` column is non-null. |
| - **`present_slots: list[str]`** — modality tags actually present (e.g. `["geometry", "geometry_single", "em_xy", "em_xz", "em_yz", "em_best"]`). |
| |
| ### Task routing & labels |
| - **`task_routing: list[str]`** — which downstream task(s) this row can serve as training data for: |
| - `false_split_correction` — merge-correction task; fires when `sample_type ∈ {merge_edit, synapse_control, adjacent_control}` AND `has_dual_mask`. |
| - `false_merge_identification` — merge-error binary classification; fires when `sample_type ∈ {split_edit, junction_control}` AND `has_single_mask`. |
| - `split_mask_generation` — pixel-level split prediction; fires when `sample_type == split_edit` AND `has_single_mask`. |
| - **`false_split_correction_label: bool`** = `same_neuron`. Populated for all rows; trainers filter by `task_routing`. |
| - **`false_merge_identification_label: bool`** = `not same_neuron`. Populated for all rows; trainers filter by `task_routing`. |
|
|
| **Usage note.** Downstream training scripts must load the appropriate geometry render per task: |
| - **Merge Correction** of false splits should use **dual-segment** renders |
| - **Split Correction** of false merges should use **single-segment** renders |
| - Furthermore, fuse A/B channels of EM images and **discard `em_best`** (it sees both labels at oblique angle and can leak ground truth) |
| |
| Otherwise, ground-truth task or label information may leak to the model and bias performance. |
| |
| ### Train/val/test split |
| - **`split: str`** — `train` / `validation` / `test`. ~80/10/10 split assigned by spatial location of the proofreading sample (`interface_point_nm`), matched via cube splits (50µm cubes tiling the volume and randomly split). |
| |
| ### Other |
| - **`metadata: str`** — JSON-stringified original metadata struct. Parse with `json.loads`. Useful keys: `operation_id`, `source_operation_id`, `strategy`, `image_types`, `interface_point_nm`, `before_root_ids`, `after_root_ids`, … |
| |
| ## Counts |
| |
| - **401,170 rows** total · ~80/11/9 train (319,727) / validation (43,517) / test (37,926) |
| - 251,499 rows with EM views; all 401,170 have geometry |
| - **~2.2M model-level samples** (EM × 4 views + geom × 3 views), or **~2.8M** counting dual + single geom separately |
| - 506 parquet shards (~240 MB each) |
| |
| ## Layout |
| |
| ``` |
| README.md |
| shards.csv metadata across shards (path, sha256, n_samples, size) |
| train/train-*.parquet WebDataset-style parquet shards with image bytes |
| val/val-*.parquet |
| test/test-*.parquet |
| metadata/ sidecar parquets with identifiers + labels (no bytes) |
| train.parquet |
| val.parquet |
| test.parquet |
| demo.parquet stratified mini-shard (one-line preview) |
| figures/ |
| channel_decomposition.png |
| ``` |
| |
| ## Sources & License |
| |
| Derived from the following upstream connectomic proofreading datasets: |
| - **MICrONS** (mouse cortex) |
| - **FlyWire** (Drosophila brain) |
| - **H01** (human cortex) |
| - Zebrafish larval connectome |
|
|
| License = `other`; users must comply with upstream licenses (which may differ across species/sources). Final outbound license will be set after upstream license review. |
|
|
| ## Citation |
|
|
| If you use ConnectomeBench2, please cite: |
|
|
| ``` |
| Brown, J., Farkas, T., Razgar, G., Boyden, E. S. |
| ConnectomeBench2: A unified benchmark for automated connectomic proofreading. |
| (2026, in submission). Brown J. and Farkas T. contributed equally as first authors. |
| ``` |
|
|
| Please also cite the upstream connectome sources used by this dataset: |
| - MICrONS (mouse cortex): https://www.microns-explorer.org/cortical-mm3 |
| - FlyWire (Drosophila): https://flywire.ai/ |
| - H01 (human cortex): https://h01-release.storage.googleapis.com/landing.html |
| - Zebrafish (fish1): https://fish1-release.storage.googleapis.com/index.html |
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|
