Bio Image Analysis.ORG

3D IMAGE SEGMENTATION WITH FOURIER NEURAL OPERATOR

Encadrants : Vannary Meas-Yedid
Email : vmeasyed@pasteur.fr
Disponible : NON
Spécialité : IMA
Nombre d'étudiants : 1
Description :

The computational complexity of 3D biomedical image segmentation often leads to out-of-memory errors in deep learning. To mitigate this, training with downsampled images is a common practice. However, this can lead to reduced accuracy when applying the model on original resolution images due to sensitivity to variations in image resolution. FNOSeg3D is introduced as a 3D segmentation model designed to be robust to variations in training image resolution. It leverages the Fourier neural operator (FNO) [1], a deep learning framework for learning mappings between functions in partial differential equations. FNO has the ability to enhance image resolution without explicit training on high-resolution data. This approach appears to be a promising advancement in the field of 3D biomedical image segmentation, particularly in terms of addressing the challenges related to image resolution variations during training.

Pré-requis : Python
Travail demandé : This project addresses an efficient 3D image segmentation in order to segment volumetric electron microscope images. After a review of the existing models in 3D segmentation, the student will implement the FNOSeg3D and make an analysis of this model.
The public datasets are available: https://cremi.org/data/, https://openorganelle.janelia.org/
Liens complémentaires : [1] Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar, “Fourier neural operator for parametric partial differential equations,” in International Conference on Learning Representations, 2021.