# Vascular Segmentation of fUS Images using Deep Learning

Hana Sebia, Thomas Guyet, Hugues Berry, Benjamin Vidal

► To cite this version:

Hana Sebia, Thomas Guyet, Hugues Berry, Benjamin Vidal. Vascular Segmentation of fUS Images using Deep Learning. Intelligence Artificielle en Imagerie Biomédicale (IABM), Mar 2025, Nice, France. hal-04964498

HAL Id: hal-04964498

https://inria.hal.science/hal-04964498v1

Submitted on 24 Feb 2025

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AIstroSight

# Vascular Segmentation of fUS Images using Deep Learning

Hana Sebia(1,2), Thomas Guyet(1,2), Hugues Berry(1,2) and Benjamin Vidal(2)

(1) AlstroSight, Centre de recherche Inria de Lyon, France (2) Université Claude Bernard Lyon 1, Villeurbanne, France

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## Contribution

* First deep learning application for fUS vascular segmentation

* Competitive segmentation performance compared to state-of-the-art

* Optimal performance with only 100 temporal frames from the fUS stack

* Strong generalization across varying brain states

* Improvement of fUS signals interpretation, supporting preclinical neurovascular research

## Functional Ultrasound Imaging (fUS)

* Captures changes in CBV related to neuronal activity

* Achieves **high spatial** and temporal resolution

* How to identify vascular compartments linked to neuronal activity?

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## Ultrasound Localisation Microscopy (ULM)

* Advanced imaging employing ultra-fast sampling to improve spatial resolution to few microns

* Requires the injection of contrast agents (Invasive)

* Use it for Automatic Annotation of fUS !

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## fUS Segmentation

* Benchmarked 7 UNet models

* UNet

* MultiresUNet

* RestNet

* RestNet

* Unet++

* UCTransNet

* Attention UNet

* TransUNet

* Used 4 loss functions

$$L_{\text{Dice\_CE}} = \alpha L_{\text{CrossEntropy}} + \beta L_{\text{Dice}}$$

$$L_{\text{CF\_V}} = \alpha L_{\text{CrossEntropy}} + \beta L_{\text{VesselDensity}}$$

$$L_{\text{CF\_B}} = \alpha L_{\text{CrossEntropy}} + \gamma L_{\text{FractalDim}}$$

$$L_{\text{CF}} = \alpha L_{\text{CrossEntropy}} + \beta L_{\text{VesselDensity}} + \gamma L_{\text{FractalDim}}$$

* $L_{\text{VesselDensity}}$ computes the **propotion of arteries** and **veins** in the image

* $L_{\text{FractalDim}}$ includes an apriori about **vessel shape complexity**

* Trained **all models** with different losses (28 configurations)

* Dataset = fUS stacks of 3000 frames **at rest**

* Metrics reported over a **7-fold cross validation**

## Best Performance

* Achieved by **Attention UNet** and **Unet++** trained with a CF loss

* Accuracy : 89%

* F1 score : 70%

* IoU: 57%

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## Visualizing vascular structures in fUS imaging

* Overlaid masks of arteries in red and veins in blue over a random fUS frame taken under **visual stimulation**

* Extracted true and predicted CBV evolution over time in the **cortex region**

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* Noticed the 4 patterns related to the response to visual stimulation

* Over all rats :

* **0.98** of correlation for **arteries** and **0.55** for **veins** in the **cortical** region

* **0.87** of correlation for **downward flow** and **0.98** for **upward flow** in the **lower** regions

## Impact of fUS Depth on performance

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* Reducing the number of frames improved metrics

* Best performance with only 100 frames

## Cross-Condition efficacy of fUS-models

* Train on rest state and test on visually stimulated session

* Used different rats for each condition

* Reached an accuracy of 90% and F1 score of 0.71%