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Large electron microscopy image datasets for connectomics are typicallycomposed of thousands to millions of partially overlapping two-dimensionalimages (tiles), which must be registered into a coherent volume prior tofurther analysis. A common registration strategy is to find matching featuresbetween neighboring and overlapping image pairs, followed by a numericalestimation of optimal image deformation using a so-called solver program.  Existing solvers are inadequate for large data volumes, and inefficient forsmall-scale image registration.  In this work, an efficient and accurate matrix-based solver method ispresented. A linear system is constructed that combines minimization offeature-pair square distances with explicit constraints in a regularizationterm. In absence of reliable priors for regularization, we show how toconstruct a rigid-model approximation to use as prior. The linear system issolved using available computer programs, whose performance on typicalregistration tasks we briefly compare, and to which future scale-up isdelegated. Our method is applied to the joint alignment of 2.67 million images,with more than 200 million point-pairs and has been used for successfullyaligning the first full adult fruit fly brain.

Joint Deformable Registration of Large EM Image Volumes: A Matrix Solver Approach