Working references, each annotated with when to use it. This is the
canonical practical reference (the former docs/sources.md verify-status table
is folded in here). When a tool’s behaviour is ambiguous, the standard/setool
docs here are the source of truth.
Verification note: source families below were verified 2026-08-10. A
specific claim still requires verifying the exact source at point of use, with a
retrieval date. Never invent a citation.
DICOM
| Resource |
What |
When to use it |
DICOM Standard — dicom.nema.org/standard.html (VERIFIED 2026-08-10, 2026c ed.) |
The normative standard (PS3.1–18). Key pages: Image Plane Module (geometry), Segmentation Image Module (SEG types). |
Settle a precise question only. For geometry use PS3.3 Image Plane Module; for pixels use Image Pixel Module + Modality LUT (Rescale Slope/Intercept). Do not browse. |
| Innolitics DICOM browser — dicom.innolitics.com |
Fast searchable browser over the standard |
Default lookup: open this tab whenever a tag appears; read its definition, VR, module. |
| pydicom — pydicom.github.io |
Python DICOM library + docs |
Every hands-on task. “Dataset basics” and the “Pixel Data” tutorial cover ~90% of needs. |
| “DICOM is Easy” — dicomiseasy.blogspot.com |
Gentle conceptual tutorial |
First read-through of Patient→Study→Series→Instance and tag/VR/length from a real example. |
Key parts to know exist (PS3.3): Image Plane Module (ImagePositionPatient,
ImageOrientationPatient), Image Pixel Module (PixelData, BitsAllocated,
RescaleSlope/Intercept), Modality LUT semantics, Multi-frame Functional Group
macros (for SEG per-frame geometry).
Physical image handling
| Resource |
What |
When to use it |
| SimpleITK fundamentals — simpleitk.org (read the fundamental concepts) |
Origin/spacing/direction, physical vs index space, resampling |
The single best reference for Ch. 2 geometry + resampling. Read the “fundamental concepts” page before any resampling code. |
| SimpleITK Resample / Transforms reference — docs |
The Resample API, interpolators, transforms |
When implementing image (BSpline/Linear) vs mask (NearestNeighbor) resampling. |
Segmentation / visualization
| Resource |
What |
When to use it |
| 3D Slicer documentation — slicer.readthedocs.io |
Loading studies, Segment Editor, volumes, transforms |
Visual QC (Ch. 2 Part E), ROI inspection (Ch. 3). Read alongside the Witowski course + the 3D Slicer companion. |
| 3D Slicer Training Compendium — training.slicer.org |
Structured step-by-step tutorials with sample data |
Companion exercises for Ch. 1–3 and learning/companion-3d-slicer.md. |
| DICOM SEG (Segmentation Storage; PS3.3 Segment Image IOD) |
SEG object model: functional groups, source-image refs, segment identity, BINARY/FRACTIONAL/LABELMAP types |
When decoding SEGs or reasoning about SEG↔mask round-trip (Ch. 2 Part C). |
| dcmqi / highdicom — github.com/QIICR/dcmqi / highdicom.readthedocs.io |
SEG encoding/decoding beyond pydicom |
When you must read/write SEGs robustly (segmentation metadata, fractional/labelmap types). |
| nnU-Net (Isensee et al., MICCAI 2021) — github.com/MIC-DKFZ/nnUNet |
Self-configuring medical-image segmentation baseline |
The strong default segmentation architecture (TotalSegmentator is built on it); read Ch. 6 Part C. |
| MONAI — monai.io |
PyTorch medical-imaging DL framework + tutorials |
2D/3D segmentation, transforms; integrates with Slicer (MONAIAuto3DSeg). |
Quantitative imaging
| Resource |
What |
When to use it |
IBSI 1 — Zwanenburg et al., Radiology 2020;295(2):328-338 — PMID 32154773 / doi (VERIFIED); ref. manual arXiv:1612.07003; ibsi.readthedocs.io |
Feature definitions, reference values, image-processing workflow |
The standardisation layer for Ch. 3. Defines what a handcrafted feature is and how to process consistently. |
IBSI 2 — Whybra et al., Radiology 2024 — doi (VERIFIED 2026-08-10) |
Standardised convolutional filters (LoG/wavelet/Riesz) + reference filtered images/feature values |
When you use filtered features — closes the gap that made wavelet features least reproducible. |
QIBA (Quantitative Imaging Biomarkers Alliance, RSNA) — rsna.org/QIBA; Profiles qibawiki.rsna.org (VERIFIED 2026-08-10) |
Profiles with a performance Claim (bias + repeatability/precision under a stated protocol) for a specific biomarker |
The broader measurement framework: FDG-PET SUV, DWI ADC, CT tumour volume change, etc. “What a measurement is worth.” |
| PyRadiomics — pyradiomics.readthedocs.io (features: features.html) |
Open-source feature extraction (image type, binWidth, resampling, feature classes) |
The implementation for Ch. 3. Pin parameters in a YAML file (as TRACE-CT does). Implements IBSI-style features with documented deviations — read the per-feature notes when strict cross-software reproducibility matters. |
| SlicerRadiomics — 3D Slicer extension |
PyRadiomics in a UI |
Build intuition interactively before scripting batch extraction. |
Relationship: QIBA characterises the measurement (bias/precision) → IBSI 1/2 define the feature/filter computation → PyRadiomics implements → SlicerRadiomics visualises.
Research reporting / validation
| Resource |
What |
When to use it |
CLAIM 2024 — Tejani et al., Radiol AI 2024;6(4):e240300 — doi (VERIFIED 2026-08-10) |
Reporting of imaging-AI model studies (classification/segmentation/reconstruction). Not for radiomics/biomarker studies. |
Ch. 7 lens for an imaging-AI model paper. |
TRIPOD+AI — Collins et al. 2024 — tripod-statement.org (VERIFIED 2026-08-10) |
Reporting of prediction-model studies (regression or ML) |
Ch. 7 lens for a prediction model. |
PROBAST+AI — Moons et al., BMJ 2025;388:e082505 — doi (VERIFIED 2026-08-10); extends/replaces PROBAST-2019 |
Risk-of-bias appraisal of prediction-model studies |
Ch. 4/7: the systematic way to find leakage paths. |
CLEAR — Kocak et al., Insights Imaging 2023;14:75 — PMID 37142815 / doi (VERIFIED 2026-08-10); + CLEAR-E3 (2024) + METRICS |
The 58-item radiomics reporting checklist (ESR/EuSoMII-endorsed) |
Ch. 3/7: the primary radiomics appraisal tool — not CLAIM. |
Framework selection by study type (Ch. 7)
- Prediction-model study (regression or ML) → TRIPOD+AI (reporting) +
PROBAST+AI (risk of bias).
- Imaging-AI model study (classification / segmentation / reconstruction) →
CLAIM 2024.
- Imaging-biomarker / radiomics study → CLEAR (NOT CLAIM 2024, which
excludes radiomics); IBSI 1/2 for feature/filter computation.
Other useful anchors
| Resource |
When to use it |
IHE — Radiology TF + AI profiles (AIW-I Rev 1.1 TI 2020, AIR Rev 1.3 TI 2025, AIRA AI Result Assessment, TI 2025) — profiles.ihe.net (VERIFIED 2026-08-10; all Trial Implementation) |
Imaging-informatics workflow & interoperability failure modes (Ch. 5); AIRA for post-deployment assessment (Ch. 9). |
| RSNA / MICCAI / SIIM educational materials; TCIA datasets & challenges |
Real data, methods, baselines (UNVERIFIED until used). |
| Bushberg, Essential Physics of Medical Imaging (recognised textbook) |
CT acquisition/reconstruction/dose depth — read when dose/reconstruction becomes an active TRACE-CT variable. |
Rules
- Never invent a citation; never copy a citation without checking when the claim
matters. Prefer DOI/PMID; record the retrieval date on verify.
- If reliable sources disagree, preserve the disagreement; do not silently
resolve it by intuition.
- Distinguish and label:
ESTABLISHED EVIDENCE · EXPERT CONSENSUS ·
COMMON PRACTICE · PLAUSIBLE INFERENCE · PROJECT HYPOTHESIS.