bioRxivScientific Communication & EducationPreprint
AFIDs-Validator: An Open-Access AI-Guided Platform for Learning Anatomical Landmark Placement
Alaa TahaiD, Dhananjhay BansaliD, Jason KaiiD, Tristan K. KuehniD, Olivia W. StanleyiD, Patrick Park, Arun Thurairajah, Mackenzie Snyder, Greydon GilmoreiD, Mohamad AbbassiD, Borna MahmoudianiD, Violet M. LiuiD, Jaime Thrower, Ali R. KhaniD, Jonathan C. LauiD
Abstract
Accurate localization of anatomical landmarks is a foundational skill in anatomy and imaging that is often taught informally through expert mentorship, requiring access to data and desktop software. There is no openly accessible, interactive resource that teaches neuroanatomy with quantitative feedback. We present the AFIDs-Validator (validator.afids.io), an open-access, browser-based platform that pairs guided instruction with quantitative assessment. The platform combines (1) a learning mode in which a language-model neuroanatomy tutor operates inside an MRI viewer, giving anatomy-first instruction that responds to the learner's current image slice, orientation, and cursor position; and (2) a validation engine that accepts a learner's landmark file and returns per-landmark Euclidean error against expert-annotated references spanning 21 brain templates. To make the feedback interpretable, we analyzed 15,000 landmark annotations across 132 human subjects and found that landmark difficulty varies fourfold (median error ranged from 0.37 mm at the anterior commissure to 1.50 mm at the temporal horns) with heavy-tailed distributions at every landmark. These distributions are compiled into per-landmark reliability priors, so learners are scored against the empirical spread of trained raters rather than an arbitrary threshold, and difficult landmarks are not mistaken for poor performance. The AFIDs-Validator requires no installation, licensed software, or local data, and all code, reference data, and tutor design are openly released.
- organism: human
- organism: non-human primate
- assay: imaging
- research focus: software / tool
- research focus: database / resource
- research focus: benchmarking
- approach: large language model
- approach: statistical model
In-depth summary
Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 1 (24 Aug 2026) · CC BY 4.0
Open browser platform embeds a language-model neuroanatomy tutor in an MRI viewer and scores landmark placements against 21 expert-annotated templates using trained-rater error distributions.
Trainees usually learn to place MRI landmarks from an expert who corrects them in person with desktop software such as 3D Slicer, an approach that does not scale and gives no quantitative feedback outside the lab. This work adds a free browser-based tutor and validator for the Anatomical Fiducials (AFIDs) protocol that judges each error against trained-rater spread at that landmark instead of one fixed cutoff.
Key results
ShownTrained-rater anatomical fiducial localization error (AFLE) varies roughly fourfold across the 32 landmarks. It is lowest at the commissures and highest at the temporal-horn and indusium griseum landmarks.
From 492 rater files over 132 human subjects in four cohorts: median AFLE PC 0.37 mm and AC 0.38 mm vs RIAMTH 1.46 mm, LIAMTH 1.40 mm, LIGO 1.37 mm and RIGO 1.24 mm; global median 0.87 mm, with 57% of placements under 1 mm and 82% under 2 mm. · 3.1 A difficulty spectrum for landmark placement; 2.5 Rater-reliability prior
ShownRater errors are heavy-tailed at every landmark, so each placement is scored against that landmark's own distribution. The same millimetre error can fall outside the expert range at one landmark and at the median at another.
A 1.2 mm error sits near the 4th precision percentile at the anterior commissure but about the 58th at the indusium griseum origin. Across landmarks, mean AFLE is 1.4 to 2.3 times the median (commissures: median about 0.37 mm, mean roughly 0.8 mm). · 3.2 Calibrated feedback relative to trained…; 3.1 A difficulty spectrum for landmark placement
ShownThe validator scores uploads against 21 expert-annotated templates in human and macaque spaces. It uses four error tiers tied to published multi-rater reliability, and learners practise in a five-step tutoring loop.
15 human and 6 macaque templates, each with all 32 landmarks (480 human, 192 macaque, 672 total points); tiers excellent <1 mm, good 1 to 2 mm, fair 2 to 4 mm, needs work ≥4 mm; the 2 mm cutoff sits near the 82nd percentile of trained-rater errors; server-side scoring <50 ms. · 2.4 Reference template library; 2.3 Validation engine; 2.2 Guided learning mode
ShownIn a worked quality-control test, the engine located a template-space mismatch by region and by landmark. It also graded severity, telling a gross MNI305 offset apart from a milder MNI152Lin one.
MNI305 vs MNI152NLin2009cAsym, aligned at AC only: mean error 2.9 mm; 13 of 32 landmarks beyond their rater 90th percentile; temporal 5.2 mm and ventricular 4.1 mm vs diencephalic floor 1.3 mm; PC 3.6 mm (4.1 times its 90th percentile). MNI152Lin mismatch: mean 1.6 mm, 23 of 32 within 2 mm. · 3.4 A quality-control example; Figure legends
ShownHow hard a landmark is to localize correlates with how much its position shifts across templates, but the two are distinct. AC-normalized positions differ across MNI templates enough that a reference template cannot be freely substituted at every landmark.
Pearson r = 0.66 over 31 non-AC landmarks (RVOH varies across templates yet is rated reliably). Across eight MNI152/MNI305 templates, AC-normalized variability averages 1.20 mm, with a minimum of 0.41 mm at RMB and a maximum of 2.35 mm at LIAMTH; AC-PC distance 27.8 to 31.0 mm (MNI152Lin 30.3 mm, MNI305 31.0 mm). · 3.3 Two kinds of difficulty across templates; 2.6 Inter-template variability analysis; Figure legends
Methods, data and reproducibility
Flask/React web app with the NiiVue viewer (MNI152NLin2009cAsym T1w) beside a streaming language-model tutor grounded by retrieval-augmented generation over AFIDs definitions. A validator scores 32-landmark FCSV/JSON files by Euclidean error, directional offsets and quality tiers, calibrated by per-landmark rater error percentiles.
- AFIDs multi-rater release (AFIDs-HCP, AFIDs-OASIS, SNSX, LHSCPD cohorts) used to derive per-landmark rater-reliability priors · human · n = 492 rater files, 132 subjects
- Human reference templates annotated with all 32 AFIDs (MNI305, MNI152 family, PD25, BigBrain and others) · human · n = 15 templates; 480 landmarks
- Macaque reference templates (D99, INIA19, MacaqueMNI, NMTv1.3, NMTv2.0asym, Yerkes19), used as validation references only · macaque · n = 6 templates; 192 landmarks
- Canonical MNI152/MNI305 templates used for AC-normalized inter-template variability analysis · human · n = 8 MNI templates
- MNI152NLin2009cAsym T1w template fetched from TemplateFlow and served in the guided learning mode · human (MNI152 template)
- Methods & tools
- Flask 3.0 (Python 3.11) backend with React 18 frontend, NiiVue WebGL2 MRI viewer, TemplateFlow, 3D Slicer Markups module (FCSV/JSON), Groq Llama 3.3 70B (default tutor model), Ollama for locally hosted open-weight models, OpenAI-compatible Python SDK (≥1.0) with streaming, Retrieval-augmented generation over AFIDs definitions, Plotly visualizations, Docker Compose and Nginx deployment, PostgreSQL via SQLAlchemy (opt-in session database), ORCID OAuth
- Software & environment
- Flask 3.0 on Python 3.11, React 18, Docker Compose with Nginx; OpenAI-compatible Python SDK (≥1.0); model set via LLM_API_KEY, LLM_BASE_URL, LLM_MODEL (default Groq Llama 3.3 70B; Ollama for local models); released under GPL-3.0.
- Experimental conditions
- Server-side error computation under 50 ms per placement; learning template served at 2 mm (∼1.7 MB) or 1 mm (∼9 MB) isotropic; a local tutor can run on institutional GPUs via Ollama, but no hardware is specified.
- Reproducibility gaps
- The tutor runs on a shared default, a user-supplied key (free Groq Llama 3.3 70B tier by default) or a self-hosted model, so explanation quality may vary with the chosen model. Effects on learner performance have not yet been measured.
Limitations (5)
- Guided mode offers only one template and one contrast, so learners never see pathological anatomy or variation in image acquisition. · stated by the authors
- The tutor's effect on learner performance has not been measured. Pre/post accuracy, transfer to individual-subject MRI, and ICC convergence studies are only planned. · stated by the authors
- Higher error at temporal-horn and indusium griseum landmarks likely reflects underlying anatomical variability as well as localization difficulty, so difficulty estimates there cannot be attributed to localization alone. · stated by the authors
- No supported model was checked for the factual accuracy or hallucination rate of the tutor's anatomical explanations, even though feedback quality depends on the configured language model. · noted in this summary
- Priors use rater distance to per-subject consensus, but learners are scored against a single template point. The abstract's figures (AC 0.37 mm, temporal horns 1.50 mm) also differ from the results (AC 0.38 mm, RIAMTH 1.46 mm). · noted in this summary
Read this if you train students or raters in MRI neuroanatomy, run multi-site landmark annotation, or want an example of an LLM tutor whose feedback is calibrated to trained-rater error distributions.
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