Abstract.A novel framework for robust 3D tracing in Electron Micrographs is presented. The proposed framework is built using ideas from hypergraph diffusion, and achieves two main objectives. Firstly, the approach scales to trace hundreds of targets without noticeable increase in runtime complexity. Secondly, the framework yields flexibility to fuse top down (global cues as hyperedges) and bottom up (local superpixels as nodes) information. Subsequently, a procedure for auto-seeding to initialize the tracing procedure is proposed. The paper concludes with experimental validation on a challenging large scale tracing problem for simultaneously tracing 95 structures, illustrating applicability of the proposed algorithm.

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Automated spatial alignment of images from different modalities is an important problem, particularly in bio-medical image analysis. We propose a novel probabilistic framework, based on a variant of the 2D hidden Markov model (2D HMM), to capture the deformation between multi-modal images. Smoothness is ensured via transition probabilities of the 2D HMM and cross-modality similarity via class-conditional, modality-specific emission probabilities. The method is derived for general multi-modal settings, and its performance is demonstrated for an application in cellular microscopy. We also present an efficient algorithm for parameter estimation. Experiments on synthetic and real biological data show improvement over state-of-the-art multi-modal image fusion techniques.

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This paper is focused on the problem of tracking cell contours across an electron micrograph stack, so as to discern the 3D neuronal structures, with particular application to analysis of retinal images. While the problem bears similarity to traditional object tracking in video sequences, it poses additional significant challenges due to the coarse z-axis resolution which causes large contour deformations across frames, and involves major topological changes including contour splits and merges. The method proposed herein applies a deformable trellis, on which a hidden Markov model is defined, to track contour deformation. The first phase produces an estimated new contour and computes its probability given the model. The second phase detects low-confidence contour segments and tests the hypothesis that a topological change has occurred, by introducing corresponding hypothetical arcs and re-optimizing the contour. The most probable solution, including the topological hypothesis, is identified. Experimental results show, both quantitatively and qualitatively, that the proposed approach can effectively and efficiently track cell contours while accounting for splitting, merging, large contour displacements and deformations.