# Exploitation de la variabilité de la population contrôle pour la détection d'anomalies non supervisée par apprentissage profond en imagerie cérébrale FDG PET

Maëlys Solal, Ravi Hassanaly, Ninon Burgos

► To cite this version:

Maëlys Solal, Ravi Hassanaly, Ninon Burgos. Exploitation de la variabilité de la population contrôle pour la détection d'anomalies non supervisée par apprentissage profond en imagerie cérébrale FDG PET. IABM 2024 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2024, Grenoble, France. hal-05443392

HAL Id: hal-05443392

https://hal.science/hal-05443392v1

Submitted on 6 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
# Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

Maëlys Solal, Ravi Hassanaly, Ninon Burgos

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

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

ARAMIS
LAB
BRAIN DATA SCIENCE

maelys.solal@icm-institute.org

X AramisLabParis

## 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 and to the lack of natural thresholding mechanism [1].

## Our contribution

Our proposed method, inspired by Z-scores, leverages the healthy population variability to overcome these limitations.

Our experiments conducted on 3D FDG PET scans from the ADNI database demonstrate the effectiveness of our approach in accurately identifying simulated Alzheimer's disease (AD) related anomalies.

## Materials & Methods

### Unsupervised anomaly detection through pseudo-healthy image reconstruction

**Training:** the model learns the distribution of healthy images, by learning to reconstruct them

**Inference:** the model is given as input an image from a subject with unknown diagnosis and it outputs a pseudo-healthy image

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

## Data

Brain 3D FDG PET scans of cognitively normal (CN) subjects

<figure><img src="image_4.png" /></figure>

**Training:** 265 subjects → 537 images

**Validation:** 53 images

**Testing:** 60 simulated AD images

## Simulation Framework

To overcome the lack of ground truth, we rely on [3] to obtain pairs of healthy (x) and abnormal images (x') by simulating AD hypometabolism

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

## Residual abnormality maps

Abnormality maps can be obtained by computing the difference between the input image (x) and its pseudo-healthy reconstruction ($\hat{x}$)

$$r = x - \hat{x} =$$

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

* No clear thresholding mechanism

* Does not account for normal variability in the population nor reconstruction errors of the model

## Z-score abnormality maps

To address the shortcomings of residuals and account for various sources of variability, we propose to rely loosely on Z-scores to generate abnormality maps

$$\tilde{z} = \frac{x - \hat{x}}{\sigma} =$$

<figure><img src="image_7.png" /></figure>

$\sigma$ is the voxel-wise standard deviation of control subjects in our training set

* Incorporates a natural thresholding mechanism

* Leverages normal variability in the population

## Experiments & Results

### Inter- & intra-subject variability

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

Voxelwise analysis - Standard deviation of healthy (CN) images in the training set (used in our Z-score)

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

Regional analysis - Comparison of the distribution of average regional FDG uptake in cognitively normal (orange) and Alzheimer's disease subjects (blue)

### Leveraging healthy population variability to design abnormality maps

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

## Contributions

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

Leverage healthy population variability

<figure><img src="image_12.png" /></figure>

Clearer association between anomalies and standard deviation

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

More natural thresholding mechanism

## Future Works

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

Enhance the generative model's capabilities

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

Account for the model's reconstruction errors

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

Extend our approach beyond Alzheimer's disease related anomalies

<figure><img src="image_17.png" /></figure>

All implementations were done using ClinicaDL, an open-source software for reproducible neuroimaging processing [4]

## References

[1] Meissen, F., Wiestler, B., Kaissis, G., and Rueckert, D., "On the Pitfalls of Using the Residual as Anomaly Score," Medical Imaging with Deep Learning, (2021)

[2] Kingma, D. P. and Welling, M., "Auto-Encoding Variational Bayes," (2014)

[3] Hassanaly, R., Bottani, S., Sauty, B., Colliot, O., and Burgos, N., "Simulation based evaluation framework for deep learning unsupervised anomaly detection on brain FDG-PET," Medical Imaging 2023: Image Processing, SPIE (2023)

[4] Thibeau-Sutre, E., Díaz, M., Hassanaly, R., Routier, A., Dormont, D., Colliot, O., and Burgos, N., "ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing," Computer Methods and Programs in Biomedicine 220, (2022)