Software

ASTEC – Adaptative Segmentation and Tracking of Embryonic Cell:  the software was developed during L. Guignard PhD thesis and later published in Contact area–dependent cell communication and the morphological invariance of ascidian embryogenesis by Léo Guignard at al., Science 2020 . It aims at providing quantitative analysis of multi-angle acquisitions of SPIM images as well as the segmentation of the temporal series of 3D images, together with quantitative informations. Download ASTEC.

Atols (Adaptative Threshold Operator based on Level Sets): Atols is a Python script allowing to detect features on images using a contrast scoring. Thus, it’s possible to detect features at different levels of intensity unlike a simple threshold which would only keep features above its value Download Atols.

CoBic-PeBic (Constrained and Penalized Biconvex algorithms for SMLM):  The code is dedicated to sparse L0 optimization in the case of deconvolution and super-resolution for SingleMolecule localization microscopy. The sparsity termis reformulated using an auxiliary variable, so that the final cost function is biconvex. We propose two algorithms CoBic (Constrained Biconvex) and PeBic (Penalized Biconvex) algorithm for SMLM, see publications link, link. CoBic is the reformulated constrained L2_L0 problem and PeBic is the reformulated penalized L2_L0 problem. Both with the positivity constraint. Github repository.

COL0RME (COvariance-based L0 super-Resolution Microscopy with intensity Estimation) The code is dedicated to the super-resolution method COL0RME (COvariancebased l0 super-ResolutionMicroscopy with intensity Estimation) that improves the spatio-temporal resolution of images acquired by common fluorescent microscopes and conventional blinking or flucutating fluorophores. The method is composed of two steps: the former where both the emitters’ independence and the sparse distribution of the fluorescent molecules are exploited to provide an accurate localization, the latter where real intensity values are estimated given the computed support. See publications link, link. Github repository.

SPARSE_CONSTRAINT_RELAX:  The optimization of a L2 term combined with an L0 constraint is made by relaxation of the initial functional with a continuous exact functional (same global minimizers). A numerical algorithm is coded for super-resolution for Single Molecule Localization Microscopy (deconvolution and super-resolution on sparse molecules). We minimize the squared norm and the relaxation Q(x), using Nonmonotone Accelerated Proximal gradient algorithm which was presented in Accelerated proximal gradient methods for nonconvex programming by Li, Huan and Lin, Zhouchen in Advances in neural information processing systems, 2015. In order to do so, we use gradient of the square norm and the proximal operator of Q(x). There are two functions for the proximal operator. One is recommended for the calculations, but the standard one to better understand what the program is doing. A failsafe function is added to ensure that a k-sparse solution is always obtained. The file uses a simulated acquistion, called Testimage.  Github repository.