Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014; 17th International C Polina Golland,Nobuhiko Hata,Robert Howe Con

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Leveraging Random Forests for Interactive Exploration of Large Histological Imagesalysis. In this paper, we introduce an interactive strategy leveraging the output of a supervised random forest classifier to guide a user through such large visual data. Starting from a forest-based pixelwise estimate, subregions of the images at hand are automatically ranked and sequentially displ

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Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumesvances are based on a two step approach: First, a set of possible 2D neuron candidates is generated for each section independently based on membrane predictions of a local classifier. Second, the candidates of all sections of the stack are fed to a neuron tracker that selects and connects them in 3D

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A Fully Bayesian Inference Framework for Population Studies of the Brain Microstructureed by extracting a scalar property from the model and subjecting it to null hypothesis significance testing. This process has two major limitations: the reported p-value is a weak predictor of the reproducibility of findings and evidence for the absence of microstructural alterations cannot be gaine

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Shading Correction for Whole Slide Image Using Low Rank and Sparse Decompositionding artifacts. A typical example of this is the unwanted seam when stitching images to obtain a whole slide image (WSI). Elimination of shading plays an essential role for subsequent image processing such as segmentation, registration, or tracking. In this paper, we propose two new retrospective sh

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Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentationthat encloses mitochondria, as well as using features that capture context over an extended neighborhood. We demonstrate that this results in both improved classification accuracy and reduced computational requirements for training.

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