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3 Janelia Publications

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    09/23/15 | Automated cerebellar lobule segmentation with application to cerebellar structural analysis in cerebellar disease.
    Yang Z, Ye C, Bogovic JA, Carass A, Jedynak BM, Ying SH, Prince JL
    NeuroImage. 2015 Sep 23;127:435-44. doi: 10.1016/j.neuroimage.2015.09.032

    The cerebellum plays an important role in both motor control and cognitive function. Cerebellar function is topographically organized and diseases that affect specific parts of the cerebellum are associated with specific patterns of symptoms. Accordingly, delineation and quantification of cerebellar sub-regions from magnetic resonance images are important in the study of cerebellar atrophy and associated functional losses. This paper describes an automated cerebellar lobule segmentation method based on a graph cut segmentation framework. Results from multi-atlas labeling and tissue classification contribute to the region terms in the graph cut energy function and boundary classification contributes to the boundary term in the energy function. A cerebellar parcellation is achieved by minimizing the energy function using the α-expansion technique. The proposed method was evaluated using a leave-one-out cross-validation on 15 subjects including both healthy controls and patients with cerebellar diseases. Based on reported Dice coefficients, the proposed method outperforms two state-of-the-art methods. The proposed method was then applied to 2(j) 77 subjects to study the region-specific cerebellar structural differences in three spinocerebellar ataxia (SCA) genetic subtypes. Quantitative analysis of the lobule volumes show distinct patterns of volume changes associated with different SCA subtypes consistent with known patterns of atrophy in these genetic subtypes.

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    07/01/15 | Post-acquisition image based compensation for thickness variation in microscopy section series.
    Hanslovsky P, Bogovic J, Saalfeld S
    IEEE 12th International Symposium on Biomedical Imaging (ISBI). 2015 Jul 01:. doi: 10.1109/ISBI.2015.7163922

    Serial section Microscopy is an established method for volumetric anatomy reconstruction. Section series imaged with Electron Microscopy are currently vital for the reconstruction of the synaptic connectivity of entire animal brains such as that of Drosophila melanogaster. The process of removing ultrathin layers from a solid block containing the specimen, however, is a fragile procedure and has limited precision with respect to section thickness. We have developed a method to estimate the relative z-position of each individual section as a function of signal change across the section series. First experiments show promising results on both serial section Transmission Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) series. We made our solution available as Open Source plugins for the TrakEM2 software and the ImageJ distribution Fiji.

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    Saalfeld LabSinger Lab
    05/28/15 | BigDataViewer: visualization and processing for large image data sets.
    Pietzsch T, Saalfeld S, Preibisch S, Tomancak P
    Nature Methods. 2015 May 28;12(6):481-3. doi: 10.1038/nmeth.3392