Gliomas are diffuse brain tumors still hardly curable due to the difficulties to identify margins. 5-ALA induced PpIX fluorescence measurements enable to gain in sensitivity but are still limited to discriminate margin from healthy tissue. In this fluorescence spectroscopic study, we compare an expert-based model assuming that two states of PpIX contribute to total fluorescence and machine learning-based models. We show that machine learning retrieves the main features identified by the expert approach. We also show that machine learning approach slightly overpasses expert-based model for the identification of healthy tissues. These results might help to improve fluorescence-guided resection of gliomas by discriminating healthy tissues from tumor margins.
In this report, we discuss the interest of quality metrics for imaging and image processing of multi-views in light sheet fluorescent 3D microscopy. Various metrics of focus are tested on real and simulated data so as to automatically assess the informational quality of the images. Application of such metrics are given for several information tasks including online control of acquisition, fast registration or image fusion. Illustrations are given for typical samples of interest for in vivo imaging with light sheet microscopy such as spheroids or organoids. We point to the reader softwares freely available under FIJI which enable to test the computation of a basic quality metric, for registration and fusion.
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