| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - biomedical |
| - electron-microscopy |
| - ssem |
| - connectomics |
| - mitochondria |
| - instance-segmentation |
| - 3d-segmentation |
| - brain |
| - cortex |
| - miccai |
| - mitoem |
| pretty_name: MitoEM - 3D mitochondria instance segmentation in ssSEM (labeled half) |
| size_categories: |
| - 1K<n<10K |
| dataset_info: |
| config_name: preview |
| features: |
| - name: sample_id |
| dtype: string |
| - name: subset |
| dtype: string |
| - name: organism |
| dtype: string |
| - name: split |
| dtype: string |
| - name: z |
| dtype: int32 |
| - name: n_instances |
| dtype: int32 |
| - name: fg_fraction |
| dtype: float32 |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: overlay |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 268684200 |
| num_examples: 80 |
| - name: val |
| num_bytes: 67196014 |
| num_examples: 20 |
| download_size: 335902375 |
| dataset_size: 335880214 |
| configs: |
| - config_name: preview |
| data_files: |
| - split: train |
| path: preview/train-* |
| - split: val |
| path: preview/val-* |
| --- |
| |
| # MitoEM (publicly-labeled half) |
|
|
| **MitoEM** — *A Large-scale 3D Mitochondria Instance Segmentation Dataset from |
| Electron Microscopy* (Wei et al., **MICCAI 2020**). Two (30 µm)³ tissue blocks |
| imaged by **serial-section multi-beam SEM (ssSEM)** at **8 × 8 × 30 nm**, one from |
| **rat** and one from **human** cortex, densely annotated for mitochondria |
| **instances**. |
|
|
| > **This mirror = the 500 publicly-labeled slices per subset (z 0–499).** |
| > Each source volume is 1000 × 4096 × 4096 voxels, but only the first half |
| > carries public ground truth. Slices z 500–999 form the challenge **test** half |
| > and their labels are **withheld** by the organizers (server-side evaluation at |
| > [mitoem.grand-challenge.org](https://mitoem.grand-challenge.org/)). They are |
| > omitted here because they cannot be evaluated offline and would double the |
| > download for no benchmark value. |
|
|
| ## Dataset Details |
|
|
| | Field | Value | |
| |---|---| |
| | Modality | Serial-section multi-beam SEM (ssSEM) | |
| | Resolution | 8 × 8 × 30 nm (x, y, z) | |
| | Task | Mitochondria **instance** segmentation (binary semantic = `label > 0`) | |
| | Subsets | `MitoEM-R` (rat), `MitoEM-H` (human) | |
| | Slices | **500 per subset** — `train` z 0–399, `val` z 400–499 | |
| | Slice size | 4096 × 4096 | |
| | Images | 8-bit grayscale PNG | |
| | Masks | uint16 TIFF (deflate), `0` = background, non-zero = instance ID | |
| | Instances | ≈10.5 k (human) / ≈5.4 k (rat) in the labeled range; ~350–390 per slice | |
| | License | **CC BY 4.0** (layered — see below; upstream tags the *annotations* MIT) | |
| | Source | `pytc/EM30` (images) + `pytc/MitoEM` (labels) — the authors' own org | |
|
|
| ## License — CC BY 4.0, not MIT |
|
|
| Both upstream repos declare **MIT**, but that covers the challenge's |
| **annotations**, not the underlying imagery. `MitoEM-H` is the `EM30-H` human |
| cortex volume, which is understood to derive from the **H01** human cortex |
| release (Shapson-Coe et al.) under **CC BY 4.0** — the attribution applied by |
| [`MedOtter/AxonEM`](https://huggingface.co/datasets/MedOtter/AxonEM), which is |
| served from the *same* `pytc/EM30` archive. This mirror is therefore tagged with |
| the most restrictive governing layer, `cc-by-4.0`, for consistency. |
|
|
| *Provenance of that call:* the H01 lineage is an upstream-attribution finding |
| carried over from AxonEM, not something re-derived here; the MICCAI 2020 paper |
| itself says only "human frontal lobe, Layer II". Tagging CC BY 4.0 is safe in |
| either case — if the imagery were governed solely by the upstream MIT tag, CC BY |
| 4.0 merely imposes a stricter attribution duty than required. |
|
|
| Both licences are permissive and redistribution-friendly; only the attribution |
| obligation differs. `MitoEM-R`'s rat volume has no separately adjudicated |
| upstream, so CC BY 4.0 is applied uniformly as the conservative choice. |
|
|
| **Attribution required:** cite the two MitoEM papers below, and credit the H01 |
| human cortex release for `MitoEM-H`. |
|
|
| ## Repository structure |
|
|
| ``` |
| MitoEM-H/ |
| im/im0000.png … im0499.png # 4096×4096 uint8 |
| mito-train-v2/seg0000.tif … seg0399.tif # 4096×4096 uint16 instance IDs |
| mito-val-v2/seg0400.tif … seg0499.tif |
| MitoEM-R/ |
| im/… mito-train-v2/… mito-val-v2/… |
| train.jsonl # canonical per-slice index |
| val.jsonl |
| dataset_metadata.json |
| ``` |
|
|
| Image `im{N}.png` pairs with mask `seg{N}.tif` for the same `N` — **both subsets |
| are already spatially aligned at 4096 × 4096**, so no cropping or offsetting is |
| needed on read. |
|
|
| ### `train.jsonl` record schema |
| ```json |
| {"sample_id": "MitoEM-H_z0000", "subset": "MitoEM-H", "organism": "human", |
| "split": "train", "z": 0, |
| "image": "MitoEM-H/im/im0000.png", |
| "mask": "MitoEM-H/mito-train-v2/seg0000.tif", |
| "shape": [4096, 4096], "n_instances": 386, "fg_fraction": 0.0508} |
| ``` |
|
|
| ## ⚠️ Geometry note — the upstream padding convention is not what it looks like |
|
|
| The upstream human images ship as `EM30-H-im-pad.zip`: **1040 slices of |
| 5120 × 5120**, while its labels are 500 slices of 4096 × 4096. The convention |
| implied by AxonEM's directory name (`pad-20-512-512`) suggests the label frame |
| sits at `[z+20, 512:4608, 512:4608]`. **It does not.** A 2D offset search scoring |
| image/mask intensity contrast against the known-aligned rat subset peaks sharply |
| at **(0, 0)**: |
|
|
| | candidate | contrast | |
| |---|---| |
| | **offset (0, 0)** | **+38.80** ← peak (decays to +25.7 by just 16 px) | |
| | offset (512, 512) | −1.32 | |
| | *shifted negative control* | −1.65 | |
|
|
| The z offset is 0 as well. The padding is appended at the **far** edges |
| (1000→1040 in z, 4096→5120 in y/x), so the padded volume's origin coincides with |
| the nominal origin — which reconciles with AxonEM's crop origins |
| (y/x ∈ {0, 1792, 3584} + 1536 = 5120; z 950 + 90 = 1040). `pad-20-512-512` |
| describes each AxonEM *crop's* internal margin, not a base-volume offset. |
|
|
| **This mirror stores both subsets already cropped and aligned**, so readers never |
| encounter the issue. It is documented only so the mirror can be reconciled |
| against upstream. |
|
|
| Two further upstream quirks handled during preparation, noted for anyone going |
| back to the source: `EM30-R-im.zip` ships 1001 `__MACOSX` AppleDouble entries |
| that a naive `glob("*.png")` double-counts to 2000 slices; and the label TIFFs are |
| *internally uncompressed* (33.5 MB each) with the zip's deflate doing all the |
| work, so extracting them verbatim yields 33.5 GB of masks. They are re-encoded |
| here as deflate TIFF (losslessly identical, ~120 KB each). |
|
|
| ## Ground truth |
|
|
| **v2** instance labels — the corrected release used by the IEEE TMI 2023 |
| challenge report. `0` = background; every non-zero value is a mitochondrion |
| instance ID. There are **no competing rater or auto-generated tiers**, so no |
| gold-standard tier selection is required. |
|
|
| - IDs are **volume-global and sparse** — they are *not* contiguous within a slice |
| (e.g. a human slice with 386 instances has IDs up to 19245). Do not assume |
| `max(id) == n_instances`. |
| - For **binary semantic** mitochondria segmentation use `mask > 0`. |
| - Annotated instances have a minimum size of **2000 voxels**. |
| - "Mitochondria-on-a-string" (MOAS) and the small/medium/large size bins from the |
| paper are **evaluation strata for error analysis, not stored label classes**. |
|
|
| ## ⚠️ Relationship to AxonEM (benchmark non-independence) |
|
|
| `MitoEM-H` and **`AxonEM-Human` are the same image volume** (`EM30-H`) — both |
| challenges serve the human images from the identical upstream archive. AxonEM is |
| already mirrored at `MedOtter/AxonEM`, where the human subset is 9 crops of that |
| volume (`EM30-H-train-9vol-pad-20-512-512`, files `im_{z}-{y}-{x}_pad.h5`). |
|
|
| Using AxonEM's crop origins and the (0, 0) alignment established above, 5 of its |
| 9 human crops intersect MitoEM's public labeled range: the four at `z=0` and the |
| one at `z=475`; the four at `z=950` fall in MitoEM's withheld test half. |
|
|
| The **annotation targets differ** (axons vs mitochondria), so this is not label |
| leakage — but the two are **not statistically independent benchmarks**, and a |
| model tuned on one has seen the other's pixels. There is no cross-reference ID |
| column; the join key is the `EM30-H` voxel origin embedded in AxonEM's filenames. |
|
|
| **`MitoEM-R` is unaffected.** AxonEM's other volume is `EM30-M` (mouse, |
| 40 × 8 × 8 nm, 750 valid slices) — a different acquisition. |
|
|
| No overlap with the other EM/microscopy sets in this suite (NucMM, UroCell, |
| CREMI, 3D-Platelet-EM, SELMA3D), nor with Lucchi/EPFL or Kasthuri++ — the latter |
| is *mouse* somatosensory cortex at 12 × 12 × 30 nm, a different species, region |
| and resolution from MitoEM-R's rat V1. |
|
|
| ## Source & citation |
|
|
| - Challenge: https://mitoem.grand-challenge.org/ |
| - Images: https://huggingface.co/datasets/pytc/EM30 |
| - Labels: https://huggingface.co/datasets/pytc/MitoEM |
| - Code: https://github.com/donglaiw/MitoEM-challenge |
|
|
| The organizers ask that **both** papers be cited: |
|
|
| ```bibtex |
| @inproceedings{wei2020mitoem, |
| author = {Wei, Donglai and Lin, Zudi and Franco-Barranco, Daniel and |
| Wendt, Nils and Liu, Xingyu and Yin, Wenjie and Huang, Xin and |
| Gupta, Aarush and Jang, Won-Dong and Wang, Xueying and |
| Arganda-Carreras, Ignacio and Lichtman, Jeff W. and Pfister, Hanspeter}, |
| title = {{MitoEM} Dataset: Large-Scale 3D Mitochondria Instance |
| Segmentation from {EM} Images}, |
| booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)}, |
| series = {LNCS}, volume = {12265}, pages = {66--76}, year = {2020}, |
| doi = {10.1007/978-3-030-59722-1_7} |
| } |
| |
| @article{shapsoncoe2024h01, |
| author = {Shapson-Coe, Alexander and Januszewski, Micha{\l} and Berger, Daniel R. and others}, |
| title = {A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution}, |
| journal = {Science}, |
| volume = {384}, number = {6696}, pages = {eadk4858}, year = {2024}, |
| doi = {10.1126/science.adk4858} |
| } |
| |
| @article{francobarranco2023mitoem, |
| author = {Franco-Barranco, Daniel and Lin, Zudi and Jang, Won-Dong and others}, |
| title = {Current Progress and Challenges in Large-Scale 3D Mitochondria |
| Instance Segmentation}, |
| journal = {IEEE Transactions on Medical Imaging}, |
| volume = {42}, number = {12}, pages = {3956--3971}, year = {2023}, |
| doi = {10.1109/TMI.2023.3320497} |
| } |
| ``` |
|
|
