Stabilizing 3D in vivo intravital microscopy images with an iteratively refined soft-tissue model for immunology experiments

• Software for stabilizing in vivo two-photon microscopy images with tissue movements.
• Pixel weighted registration algorithm that explicitly treats inter and intrastack motion errors.
• A nonlinear soft-tissue deformation alignment correction called the poor man׳s diffeomorphic map.
• A method for detecting and removing multiple exposure errors caused by undersampling.
• A globally stabilization method using a constrained optimization method.

We describe a set of new algorithms and a software tool, StabiTissue, for stabilizing in vivo intravital microscopy images that suffer from soft-tissue background movement. Because these images lack predetermined anchors and are dominated by noise, we use a pixel weighted image alignment together with a correction for nonlinear tissue deformations. We call this correction a poor man׳s diffeomorphic map since it ascertains the nonlinear regions of the image without resorting to a full integral equation method. To determine the quality of the image stabilization, we developed an ensemble sampling method that quantifies the coincidence between image pairs from randomly distributed image regions. We obtain global stabilization alignment through an iterative constrained simulated annealing optimization procedure. To show the accuracy of our algorithm with existing software, we measured the misalignment error rate in datasets taken from two different organs and compared the results to a similar and popular open-source solution. Present open-source stabilization software tools perform poorly because they do not treat the specific needs of the IV-2pM datasets with soft-tissue deformation, speckle noise, full 5D inter- and intra-stack motion error correction, and undefined anchors. In contrast, the results of our tests demonstrate that our method is more immune to noise and provides better performance for datasets’ possessing nonlinear tissue deformations. As a practical application of our software, we show how our stabilization improves cell tracking, where the presence of background movement would degrade track information. We also provide a qualitative comparison of our software with other open-source libraries/applications. Our software is freely available at the open source repository http://sourceforge.net/projects/stabitissue/.