--- 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 **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} } ```