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Imaging Evidence Path

Medical imaging AI — reference

Reference · The task-family map and verified reading list. Teaching versions live in the lessons.

The task map

Task What it produces Key risk
Classification a label per study/series/lesion spectrum bias, miscalibration
Detection bounding boxes / localisation of findings false positives, small-object misses
Segmentation a voxel mask per structure/lesion overlap metrics hide boundary errors; reader variability
Registration a spatial transform aligning two images silently wrong alignment; FoR misuse (Ch. 2)
Reconstruction an image from raw/sparse data changes quantitative values downstream
Denoising / enhancement a cleaner/sharper image inventing structure; losing pathology
Harmonisation images/features normalised across sites/scanners removing real signal; leakage
Image synthesis a synthetic image of an unacquired contrast/sequence/dose hallucinating absent information
Quantitative measurement a number (volume, diameter, attenuation) geometry/segmentation-dependent
Prognosis / prediction a risk/forecast leakage, overfitting, calibration
Report generation free-text / structured findings fabricated findings; grounding
Multimodal (image+text+clinical) fused prediction/report leaking outcome via clinical fields
Workflow / agent orchestration coordinated multi-step pipeline compounding errors, no provenance

Most real systems combine several. The radiomics path is segmentation → measurement → phenotype → prediction; the foundation-model direction is one model, many tasks.

Segmentation metrics

  • Dice / IoU — overlap; size-dependent.
  • Hausdorff / 95%-Hausdorff / mean surface distance — surface error; complement overlap.
  • Judge by downstream measurement, not one overlap number; report reader spread.

Tools / models

  • nnU-Net (Isensee et al., Nat Methods 2021;18(2):203-211, PMID 33288961) — self-configuring baseline; ESTABLISHED EVIDENCE as the strong default.
  • TotalSegmentator (Wasserthal et al., Radiol AI 2023;5(5):e230024, PMID 37795137) — ~104+ structures in one pass; MRI variant PMID 39964271.
  • MONAI — PyTorch framework + tutorials for medical-imaging DL; integrates with 3D Slicer (MONAIAuto3DSeg).

Reconstruction

FBP → IR → DL. IR alters texture; DL can alter/invent texture. Record the method; do not pool across regimes; test under deployment reconstruction. TRACE-CT classify_source_role() tags lung_1_25mm vs standard_5_0mm.

Synthetic imaging — verified reading

(literature map “Synthetic imaging” section):

  • Spadea et al., Med Phys 2021, PMID 34407209 (MR→CT).
  • Sim et al., Cureus 2025;17(10):e94561, PMID 41246632 (shoulder MRI DL-recon).
  • Park et al., Eur Radiol 2025;35(12):7867-7876, PMID 40483292 (lumbar CT denoising).
  • Herrmann et al., Diagnostics 2023;13(17):2747, PMID 37685285 (elbow MRI DL-recon).
  • “RaD”, arXiv:2412.01496 (perceptual realism ≠ task utility).

Foundation / generalist models — verified

  • TotalSegmentator (PMID 37795137) — infra layer.
  • Merlin — Nature 2026, doi:10.1038/s41586-026-10181-8, PMID 41781626 (preprint PMID 38978576). CT 3D VLM; large external validation.
  • a2z-1 — arXiv:2412.12629 (preprint); generalist abdomen-pelvis CT.
  • OmniMRI — arXiv:2508.17524 (preprint, 2025-08-24); generalist MRI VLM.
  • Paschali et al., Radiology 2025;314(2):e240597, PMID 39903075 — read first for landscape/limits.
  • ReCo — medRxiv 2026.07.14.26358025 (preprint); agentic/MCP orchestration example.

Progression: task-specific → pretrained → foundation → VLM → generalist → agentic. Ask of any paper: data scope, zero-shot vs adapted, external testing, licence, domain-shift limits. Most are preprints — read for evaluation methodology, treat “generalist” as orientation.

What usually goes wrong

  • Ignoring anisotropic spacing in architecture/augmentation; augmentation that breaks HU/physical meaning.
  • Reporting Dice without surface metrics or downstream-task impact.
  • Pooling reconstruction methods; claiming generalisation from in-house data only.
  • Treating a texture feature as a biomarker; treating a realistic synthetic image as “correct”; trusting a VLM’s generated finding without grounding.
  • Deploying an agent/MCP pipeline with no provenance or audit (Ch. 5).

TRACE-CT connection

  • TotalSegmentator-class segmentation → measurement is the spine TRACE-CT’s radiomics pipeline assumes (the SEG objects it decodes).
  • Reconstruction as a variable (classify_source_role()) is the reconstruction-changes-features effect, made real (Ch. 3).
  • Foundation/generalist and synthetic-imaging items stay as reading; nothing in the current bounded experiment changes.

What to retain (chapter summary)

  1. Medical images are spatial, anisotropic, scarce, and acquisition-dependent; geometry is part of the input.
  2. Segmentation: judge by downstream measurement, not one overlap number; reader variability contextualises the reference standard (not a hard ceiling); nnU-Net/TotalSegmentator/MONAI are the tools.
  3. Reconstruction (FBP→IR→DL) changes quantitative values — record it.
  4. Synthetic imaging: “what is present vs what must be invented”; evaluate beyond pixel realism; hallucination/invented-information is the defining risk.
  5. Foundation/generalist progression: task-specific → pretrained → foundation → VLM → generalist → agentic; read for data scope, external testing, and domain shift.