Quantification in optical sectioning microscopy: a comparison of some deconvolution algorithms in view of 3D image segmentation

Optical microscopy allows us to study living fluorescent biological samples. Optical sectioning is a technique to obtain three-dimensional (3D) information about the observed object by acquiring a stack of two-dimensional (2D) images at different depths through the sample. However, the specific shape of the 3D optical transfer function of the optical microscope leads to images presenting defects, such as, for example, an apparent elongation along the vertical axis. It is therefore necessary to preprocess the images before any quantitative measurement is performed. This image restoration can be obtained by deconvolution of the acquired 3D image. We have tested several deconvolution algorithms on synthetic images, obtained by convolution of a solid sphere with a measured point spread function. We have compared the restored image with the original one (shape and volume). The linear least-squares method is fast, but artefacts are still present in the restored images. The Carrington method is well adapted to thin objects. The maximum likelihood - expectation maximization method leads to a good reconstruction of the object, but is very time consuming. Resume. La microscopie optique permet l'etude de specimens biologiques vivants et fluorescents. La technique par coupes seriees donne des informations tridimensionnelles (3D) sur l'objet etudie par l'acquisition d'une pile d'images bidimensionnelles a differentes profondeurs de focalisation a travers l'echantillon. Les specificites de la fonction de transfert optique 3D du microscope conduisent a des images presentant des defauts, comme par exemple une elongation apparente selon l'axe vertical. Il est donc necessaire de traiter les images avant toute mesure quantitative. On procede a une deconvolution de l'image 3D obtenue. Nous avons teste differents algorithmes de deconvolution sur des images de synthese obtenues par convolution d'une bille pleine avec une fonction de transfert optique mesuree. Nous avons compare, en forme et en volume, les images restaurees avec l'image d'origine. La methode `linear least square' est rapide, mais l'image restauree presente des artefacts. La methode de Carrington est bien adaptee a la restauration d'objets fins. La methode `maximum likelihood - expectation maximization' permet une bonne reconstruction des images, mais demande de grands temps de calcul.