Real footage · NASA materials evaluation lab

Real footage · NASA materials evaluation lab

Train inspection AI
on reviewed 3D scans.

Label CT and MRI scans, review model suggestions and keep the exact dataset behind each training run.

WorldAerospace · inspection lab

Public scans, model proposals and licensed footage. Sources

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One scan, layer by layer.

A real scan, what a model computed and proposed from it, the label people published, and the record that keeps them apart.

Aerospace · industrial CTFraunhofer sample V5, a historic airframe section, axial slice 300
  1. 01 Scan slice

    Axial slice 300 of the real volume, windowed: the model's input, with the one box it was given.

  2. 02 Encoder features, PC1

    SAM 2.1's image encoder turns the slice into 256 channels on a 64 x 64 grid. This is their first principal component (17% of the variance), at its native 16 px blocks. A projection, not a layer.

  3. 03 Mask logits

    The mask decoder's output, -25 to +8. Logits are not probabilities.

  4. 04 Model proposal

    Where the logits are above zero: SAM 2.1's outline of the bottle wall, dashed.

  5. 05 Published label

    Instance 138, the pressure bottle, as published by the dataset's annotators (solid), with neighbouring parts' published instance outlines (thin). IoU with the proposal: 0.82.

    Labels on the picture: 148 · 146 · 140 · 139
  6. Aerospace · V5scan fba9a5cfSAM 2.1 proposal 68d34799published label 138 1c7047f3labeldataset maintag dv-sample-01Training input previewreads dv-sample-01
    • Aerospace · V5scan fba9a5cfSAM 2.1 proposal 68d34799, on its own branchpublished label 138 1c7047f3, merged
    • dataset main, tag dv-sample-01, read by the training input preview

    06 Record

    The history for this scan: the proposal stays on its own branch, each published human label joins the dataset line on its own, and a tag pins the version a training job reads.

CT and instance masks: Gruber et al., Fraunhofer EZRT, doi 10.5281/zenodo.10651746, CC BY 4.0 (opens in a new tab). Model: SAM 2.1 hiera-tiny, run by Nitsor outside the product. Modified by Nitsor: windowed, cropped, outlines and model outputs drawn over it.

Healthcare · clinical CTLIDC-IDRI-0003, axial slice 64, nodule field about 112 x 63 mm
  1. 01 Scan slice

    Slice 64 of a public chest CT, lung window. The same patient the four radiologists outlined. The box given to the model is the readers' combined extent plus 3 mm, derived from their outlines.

  2. 02 Encoder features, PC1

    SAM 2.1's image encoder, first principal component of 256 channels (25% of the variance), native 16 px blocks.

  3. 03 Mask logits

    SAM 2.1's decoder output for one box around the nodule. Logits, not probabilities.

  4. 04 Lung lobes, a second model

    A second model, a different task: lung lobes. TotalSegmentator's softmax for the class 'left lower lobe' on the same field, per voxel. It does not look for the nodule.

  5. 05 Model proposal

    SAM 2.1's outline of the nodule, dashed. IoU with the four readers: 0.79, 0.80, 0.84, 0.84.

  6. 06 Four published outlines

    Four radiologists' outlines, kept separate, over their agreement (grey, 1 to 4). Longest diameter of the 2-of-4 consensus: 32.8 mm, derived from the outlines.

  7. Healthcare · LIDC-0003scan 58628ab1SAM 2.1 proposal 96cab4b8reader 1 aa3e882ereader 2 9e9a9479reader 3 55e99aaareader 4 1a30ac074 readers, kept separatedataset maintag dv-sample-01Training input previewreads dv-sample-01
    • Healthcare · LIDC-0003scan 58628ab1SAM 2.1 proposal 96cab4b8, on its own branchreader 1 aa3e882e, mergedreader 2 9e9a9479, mergedreader 3 55e99aaa, mergedreader 4 1a30ac07, merged
    • dataset main, tag dv-sample-01, read by the training input preview

    07 Record

    The history for this scan: the proposal stays on its own branch, each published human label joins the dataset line on its own, and a tag pins the version a training job reads.

32.8 mm, longest diameter of the 2-of-4 consensus, derived from the published outlines

CT and reader outlines: Armato et al., "Data From LIDC-IDRI", TCIA, doi 10.7937/K9/TCIA.2015.LO9QL9SX, CC BY 3.0 (opens in a new tab). Models: SAM 2.1 hiera-tiny and TotalSegmentator v2, run by Nitsor outside the product. Modified by Nitsor: windowed, cropped, outlines and model outputs drawn over it.

Healthcare · MRISPIDER case 101, sagittal T2 MRI, slice 8
  1. 01 MRI slice

    A sagittal T2 image from SPIDER case 101, slice 8 across. The display keeps the scan's own voxel spacing; the image and the masks share one geometry.

  2. 02 Published masks

    Solid white: the published vertebra (01 to 06) and disc (201 to 206) masks on this slice. The dataset made them semi-automatically, then its annotators reviewed and manually corrected them. The numbers count from the bottom up; they are not anatomical names.

Lumbar MRI and reference masks: SPIDER, van der Graaf et al., Radboud University Medical Center, doi 10.5281/zenodo.10159290, CC BY 4.0 (opens in a new tab). Modified by Nitsor: cropped, and the published masks drawn as outlines. No model output is drawn on this scan.

Food · fruit CTNavel orange SW03, CT slice 358, the equator
  1. 01 Scan slice

    The equatorial slice of a real orange CT: rind, pulp segments and the central column.

  2. 02 Encoder features, PC1

    SAM 2.1's image encoder, first principal component of 256 channels (32% of the variance), at its native blocks.

  3. 03 Mask logits

    The decoder's output for one box, -9 to +7. Logits are not probabilities.

  4. 04 Model proposal

    SAM 2.1's recorded proposal, long dashes. It takes in the rind; the authors' scripted pulp mask stops inside it.

  5. 05 Authors' scripted masks

    Short dashes: the pulp and oil-gland masks, the authors' scripted segmentation (thresholding and morphology, parameters set by hand for each fruit and checked by eye), shipped with the scans; no person drew them. IoU 0.76 compares SAM 2.1 with the scripted pulp mask, not with a human label.

Citrus CT and its scripted masks: Amézquita, Quigley, Ophelders, Seymour, Munch, Chitwood, doi 10.5061/dryad.34tmpg4n6, CC0 (opens in a new tab). Model: SAM 2.1 hiera-tiny, run by Nitsor outside the product. Modified by Nitsor: cropped, and SAM 2.1's proposal and the authors' scripted masks drawn as dashed outlines.

Hashes: sha256 prefixes of the published files.

See the workspace

Proposal

Proposed by
SAM 2.1, from one box
Slices
64, from slice 300
State
recorded, awaiting review

Model proposals from SAM 2.1 (Apache 2.0 (opens in a new tab)), run outside Nitsor. Real industrial CT, Gruber et al., Fraunhofer EZRT, CC BY 4.0 (opens in a new tab), modified: windowed, cropped, the proposals drawn over it.

Proposals

Review model suggestions against the scan.

Models and agents are first-class in Nitsor. Mark one slice and a model proposes masks on up to 64 slices; reviewers accept, correct or reject each one instead of drawing it.

See model proposals

Review, one nodule

Proposed by
SAM 2.1, from one box
Outlined by
four radiologists, kept separate
Longest diameter
32.8 mm, derived
Reviewer
P. Kowalski

The outlines are the LIDC readers' own, as published.

Four published reader outlines and a model proposal from SAM 2.1 (Apache 2.0 (opens in a new tab)), outside Nitsor. The box the model was given is the readers' combined extent plus 3 mm, derived from their outlines. Real chest CT, Armato et al., "Data From LIDC-IDRI", TCIA, CC BY 3.0 (opens in a new tab), modified: lung-windowed, cropped, the outlines redrawn, the agreement map and diameter derived.

Review

See which revision the reviewer checked.

A review stays attached to the label revision inspected. Rejected work stays visible, nobody reviews their own, and uncertainty scores can send the least certain work to reviewers first.

See how review works
Aerospace · V5scan fba9a5cfSAM 2.1 proposal 68d34799published label 138 1c7047f3labelHealthcare · LIDC-0003scan 58628ab1SAM 2.1 proposal 96cab4b8reader 1 aa3e882ereader 2 9e9a9479reader 3 55e99aaareader 4 1a30ac074 readers, kept separateFood · apple 98scan cb1c419aSAM 2.1 proposal 14bc3a32browning score 10/10, no mask f1c1903dscan + scoredataset maintag dv-sample-01Training input previewreads dv-sample-01
  • Aerospace · V5scan fba9a5cfSAM 2.1 proposal 68d34799, on its own branchpublished label 138 1c7047f3, merged
  • Healthcare · LIDC-0003scan 58628ab1SAM 2.1 proposal 96cab4b8, on its own branchreader 1 aa3e882e, mergedreader 2 9e9a9479, mergedreader 3 55e99aaa, mergedreader 4 1a30ac07, merged
  • Food · apple 98scan cb1c419aSAM 2.1 proposal 14bc3a32, on its own branchbrowning score 10/10, no mask f1c1903d, joined as a score, with no mask
  • dataset main, tag dv-sample-01, read by the training input preview

Real scans and published labels, modified (windowed, cropped, outlines drawn): Gruber et al., Fraunhofer EZRT, CC BY 4.0 (opens in a new tab); Armato et al., "Data From LIDC-IDRI", TCIA, CC BY 3.0 (opens in a new tab); Schut et al., CWI and GREEFA, CC BY 4.0 (opens in a new tab). Proposals from SAM 2.1 (Apache 2.0 (opens in a new tab)), outside Nitsor. Hashes: sha256 prefixes of the published files.

Datasets

Keep training data fixed as labels change.

Give each training job a fixed dataset version. It keeps reading the same scans and labels while your team continues editing.

See versioning

The system of evidence for AI-assisted inspection

Identify the scans, labels and reviews behind each training dataset.

The Nitsor editor steps through slices of a chest CT; a blue nodule label is selected and framed on the scan.
What each frame shows
  1. Step through the slices of a chest CT
  2. Select a label to find it on the scan
  3. Each label is saved with its scan and slices
Label, assign and review in one workspace. A volumetric editor for CT and MRI. It opens uncompressed DICOM and VGStudio volumes and draws each view as slices arrive.

Real chest CT: Armato et al., "Data From LIDC-IDRI", TCIA, CC BY 3.0 (opens in a new tab), modified: cropped. Labels from the LIDC readers' published annotations.

For agents

Agents call the API with a credential of their own.

Real open scans. Sources

Why Nitsor

When someone asks which labels trained the model, you have an answer.

  • Who changed this label, and who checked it?

    Every revision keeps its author, and each verdict on a label records the version of the label it judged.

  • Where do our scans go?

    In your storage. The browser reads viewed slices from there through short-lived signed links, and model jobs use temporary copies.

  • Do we pay for every reviewer?

    No. No plan charges per person, on Community, Team or Enterprise.

Solutions

For teams working with CT and MRI.

Castings, welds, composites, material samples and research specimens, with every label and review tied to the scan it was drawn on.

Batteries, additive parts, food, rock cores, cultural heritage and more. Tell us what you inspect

Real scans and their labels, each modified (windowed, cropped, outlines drawn): Gruber et al., Fraunhofer EZRT, CC BY 4.0 (opens in a new tab); Kopp et al., MIT, CC0 (opens in a new tab); Armato et al., "Data From LIDC-IDRI", TCIA, CC BY 3.0 (opens in a new tab); van der Graaf et al., SPIDER, Radboud UMC, CC BY 4.0 (opens in a new tab); Amézquita et al., Michigan State University, CC0 (opens in a new tab); Wimmer et al., Bern University Hospital, CC BY 4.0 (opens in a new tab). Sources

Show us what you inspect.

Start with a public CT or MRI scan. See how a model suggestion becomes a reviewed label and a fixed training dataset.