# Étude de la variabilité des modèles et construction de cartes d'anomalies tenant compte de l'incertitude à l'aide de modèles génératifs profonds non supervisés dans le cadre de la TEP-FDG en 3D du cerveau

Maëlys Solal, Ravi Hassanaly, Ninon Burgos

► To cite this version:

Maëlys Solal, Ravi Hassanaly, Ninon Burgos. Étude de la variabilité des modèles et construction de cartes d'anomalies tenant compte de l'incertitude à l'aide de modèles génératifs profonds non supervisés dans le cadre de la TEP-FDG en 3D du cerveau. IABM 2025 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2025, Nice, France. hal-05444797

HAL Id: hal-05444797

https://hal.science/hal-05444797v1

Submitted on 12 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Studying model variability in VAE-based unsupervised anomaly detection in 3D FDG-PET of the brain

<figure><img src="image_2.png" /></figure>

ARAMIS
LAB
BRAIN DATA SCIENCE

Maëlys Solal, Ravi Hassanaly, Hugues Roy, Ninon Burgos

Sorbonne Université, Paris Brain Institute, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France

<figure><img src="image_3.png" /></figure>

maelys.solal@icm-institute.org

## Context

Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data. It allows identifying a wide variety of anomalies from unlabelled data.

It relies on reconstructing a subject-specific image of healthy appearance to which the subject's real image can be compared to detect anomalies.

## Problem setting

In the literature, it is common for anomaly detection to rely on analysing the **residual image** between the subject's real image and its pseudo-healthy reconstruction.

This approach however has limitations partly due to the pseudo-healthy reconstructions being imperfect.

## Our approach

Building upon prior studies exploring normal variability within the healthy population, we investigate model variability with the aim to design abnormality maps that are less sensitive to unwanted sources of variability.

<figure><img src="image_4.png" /><figcaption>## Unsupervised Anomaly Detection & Pseudo-Healthy Reconstruction</figcaption></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

Brain 3D FDG PET scans of healthy subjects

**Training:** 302 subjects → 545 images

**Validation:** 25 images

**Testing:** 50 simulated AD images

<figure><img src="image_7.png" /><figcaption>## (2) Model's reconstruction errors</figcaption></figure>

<figure><img src="image_8.png" /></figure>

## Experiments & Results

**Hypometabolism simulation:** to overcome the lack of ground truth and obtain pairs of healthy and diseased images

<figure><img src="image_9.png" /></figure>

**Training:** 9 models using 3 different random seeds and 3 different data splits (obtained with cross-validation)

<figure><img src="image_10.png" /></figure>

**Tasks:** (a) AD vs CN classification, (b) simulated AD anomaly detection

<table><thead><tr><th rowspan="2">Abn. map</th><th colspan="3">Classification</th><th>Anom. detect.</th></tr><tr><th>Acc.</th><th>Sens.</th><th>Spec.</th><th>NCC w. sim. mask</th></tr></thead><tbody><tr><td>PET images</td><td>0.958</td><td>0.933</td><td>0.983</td><td></td></tr><tr><td>r</td><td>0.842</td><td>0.950</td><td>0.733</td><td>0.528 ± 0.072</td></tr><tr><td>z<sub>all</sub></td><td>0.633</td><td>0.983</td><td>0.283</td><td>0.559 ± 0.062</td></tr><tr><td>z<sub>splits</sub></td><td>0.500</td><td>0.000</td><td>1.000</td><td>0.242 ± 0.058</td></tr><tr><td>z<sub>seeds</sub></td><td>0.500</td><td>0.000</td><td>1.000</td><td>0.268 ± 0.048</td></tr><tr><td>z<sub>diag</sub></td><td>0.500</td><td>0.000</td><td>1.000</td><td>0.256 ± 0.050</td></tr><tr><td>z<sub>HC</sub></td><td>0.892</td><td>0.983</td><td>0.800</td><td>0.586 ± 0.067</td></tr></tbody></table>

## References

Solal et al, “Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET” SPIE Medical Imaging 2024

Hassanaly et al, “Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET” MELBA Special Issue for Generative Models, 2024

All data processing and implementations were done using Clinica and ClinicaDL

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Z-score based abnormality maps** obtained from models trained with different random seeds and data splits</figcaption></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>