Annonce

Partners: IBISC (Univ Évry, Université Paris-Saclay), CHSF Corbeilles, CHRU de Tours

Basic AI and Data Science: statistical training in big dimensions

Specialized ML and AI: signal, image, vision

Application domain: precision medicine, imagery by MR

Mots-clés deep learning, multi-modality imaging, semi-supervised learning

Key-words machine learning, deep tech, neuroimaging, precision medicine, stroke,

multimodality, template standadization

Total duration of internship: 6 months (graduate) or 3 months (undergraduate)

Working period: From 2024/02/01 to 2024/09/01

Context and objectives

According to the World Health Organization, stroke is the second cause of death, the first for

women, and the leading cause of chronic functional disability in adults, with 17 million victims,

31% of whom were under 65.

In France, around 150,000 people are hospitalized yearly for a stroke, one every 4 minutes. It

represents a financial burden of about 2.8 billion €/year; in reality, 10 billion over five years due

to the cost of disability.

Ischemic stroke is caused by a blood clot (thrombus) that blocks a brain artery, causing a lack of

oxygen to brain tissue supplied by that artery. There is an urgent need to diagnose and

determine the choice of treatment.

Neuroimaging plays a decisive role in demonstrating acute ischemia with the concept of

ishemic penumbra. It has been shown that the growth, duration, and infarct volume of

ishemic stroke directly influences the amount of irreversible tissue loss. There is a need for

a quantitative penumbra-assessment stroke protocol with the minimum possible imaging

time that delivers the maximum possible information. Today, this assessment is made with

perfusion imaging, with the risk of exposure to radiation and use of iodinated contrast

medium, which may affect renal-impaired patients. For this reason, finding other solution

have been a focus of research. An MR-based fast, non-invasive stroke protocol such as T2*

gradient-echo and susceptibility-weighted imaging (SWI) are proposed solutions in the

literature to quantitatively measure the penumbra.

Objectives

The solution we want to implement is based on automatically segmenting the area of already

dead tissue (infarct) and ischemic tissues at risk (penumbra), as seen in Figure 1. The penumbra

assessment can be obtained through the mismatch ratio obtained from the MR perfusion

-weighted imaging (PWI) and the diffusion-weighted imaging (DWI) modalities. Recent studies

have proposed a framework for accurate quantitative assessment of penumbra using SWI-DWI

and its validation with PWI-DWI-based quantification [1].

These assessments depend heavily on the user expertise. The current study aims to develop an

automated image-processing framework for quantifying penumbral volume using SWI and/or

DWI [2]. Applying AI algorithms to the analysis of MR images makes it possible to work on large

amounts of data in a more relevant way than conventional statistical methods. The objectives

are (1) to validate the results on an extensive patient database (2) to integrate the model into

clinical application software with a user-friendly interface. The current study also includes the

study of the penumbra volume as a prognosis tool in the case of revascularization.

Methodology

No automatic method of quantification of penumbra volume using SWI and DWI has been

published to date. There are a few publications on semi-automatic segmentation of the lesion

by angiography [4, 10, 7, 12, 11]. Only the last three relate to the segmentation of the lesion in

the brain. These studies all have in common that they need a “manual” seed to run the

algorithm, meaning they cannot find the position of the lesion on their own.

Expected results

The expected solution will better characterize the ischemic mismatch by associating multiple

weak MRI signals with the definition of pathology. The MR mismatch will improve the reading

quality of current images by performing analyses that are not currently carried out because they

take too long to execute manually, such as the volumetric measurement of the penumbra.

Moreover, the MR mismatch solution without a contrast agent will make this analysis quicker

and available to all types of patients.

This solution will determine what information in the image implies specific treatments leading to

a better patient prognosis. AI can help the radiologist prioritize urgent cases by deciding which

imaging tests to assess first. Ultimately, radiology experts should give more information than

the human eye on the texture of the thrombus and its accessibility for recanalization

treatments.

Expected performance criteria:

Evaluating the new procedure against a referenced approach raises many methodological

difficulties. The expected performance indicators are (1) the repeatability of the deterministic

segmentation process in a degraded situation or not, (2) the efficiency of the tool to be tested

on a ground truth basis and quantified with DICE [3] to measure performance in segmentation,

(3) a speed of execution of a few minutes.

References

[1] Bhattacharjee R, Gupta RK, Das B, Dixit VK, Gupta P, Singh A. Penumbra quantification from MR SWI

-DWI mismatch and its comparison with MR ASL PWI-DWI mismatch in patients with acute ischemic stroke.

NMR Biomed. 2021 Jul;34(7):e4526. doi: 10.1002/nbm.4526. Epub 2021 Apr 20. PMID: 33880799.

[2] Yamaguchi S, Horie N, Morikawa M, Tateishi Y, Hiu T, Morofuji Y, Izumo T, Hayashi K, Matsuo T.

Assessment of veins in T2*-weighted MR angiography predicts infarct growth in hyperacute ischemic

stroke. PLoS One. 2018 Apr 4;13(4):e0195554. doi: 10.1371/journal.pone.0195554. PMID: 29617449;

PMCID: PMC5884555.

[3] I. Brahim, D. Fourer, V. Vigneron and H. Maaref, "Deep Learning Methods for MRI Brain Tumor

Segmentation: a comparative study," 2019 Ninth International Conference on Image Processing Theory,

Tools and Applications (IPTA), 2019, pp. 1-6, doi: 10.1109/IPTA.2019.8936077.

[4] J. Egger, J., O’Donnell, T., Hopfgartner, C., Freisleben, B. (2009). Graph-based Tracking Method for

Aortic Thrombus Segmentation. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds) 4^th

European Conference of the International Federation for Medical and Biological Engineering. IFMBE

Proceedings, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89208-3_139

[5] Kobold, J. Vigneron, V. Maaref, H. Fourer, D., Aghasaryan, M. Alecu, C. Chausson, N. L’hermitte, Y.

Smadja, D. and Läng, E. Stroke Thrombus Segmentation on SWAN with MultiDirectional U-Nets. In 9^th

IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA 2019), Istanbul,

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