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Communication Dans Un Congrès Année : 2022

TDA-Clustering Strategies for the Characterization of Brain Organoids

Résumé

We propose to use Topological Data Analysis (TDA) to in- tent deciphering the morphological development of these cultures seg- mented with U-Net. To classify various shape at three developmental stages, we propose to combine TDA with a Kmean Clustering or with a Support Vector Machine. We calculated some characteristics as regres- sions on the rendered presentations to compare mean representations from each stage. Results show a specific morphological pattern (whatever the kind of TDA clustering) appears between 9 and 14 days correspond- ing to the neuroepithelial formations which has to be further studied and validated on an other dataset. Hence, our method provides an indicator of neuroepithelial growth formation stage prediction in brain organoids, it has to be compared with others classification methodologies.
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Dates et versions

hal-03806799 , version 1 (08-10-2022)

Identifiants

  • HAL Id : hal-03806799 , version 1

Citer

Clara Brémond Martin, Camille Simon Chane, Cedric Clouchoux, Aymeric Histace. TDA-Clustering Strategies for the Characterization of Brain Organoids. IEEE MICCAI 2022 (TDA4MedicalImaging Workshop), Sep 2022, Singapour, Singapore. ⟨hal-03806799⟩
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