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Let $$\mathcal{D}$$ be a dataset of smooth {3D}-surfaces, partitioned into disjoint classes $$\mli{CL}_j$$, $$j= 1, \ldots, k$$. We show how \emph{optimized diffeomorphic registration} applied to large numbers of pairs $$S,S' \in \mathcal{D}$$ can provide descriptive feature vectors to implement automatic classification on $$\mathcal{D}$$, and generate classifiers invariant by rigid motions in $$\mathbb{R}^3$$. To enhance accuracy of automatic classification, we enrich the smallest classes $$\mli{CL}_j$$ by diffeomorphic interpolation of smooth surfaces between pairs $$S,S' \in \mli{CL}_j$$. We also implement small random perturbations of surfaces $$S\in \mli{CL}_j$$ by random flows of smooth diffeomorphisms $$F_t:\mathbb{R}^3 \to \mathbb{R}^3$$. Finally, we test our automatic classification methods on a cardiology data base of discretized mitral valve surfaces. 

We study the performance of CLAIRE—a diffeomorphic multi-node, multi-GPU image-registration algorithm and software—in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality—but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions. 

Our work targets automated analysis to quantify the growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate the proposed automatic cell tracking algorithm using (i) recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps and (ii) real data. On a batch of 1100 simulated image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences (i.e., real data) of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.