Bioimage Analysis Proceedings Session at #ISBMECCB (Live Blogging)

This afternoon, I sat in the Bioimage Informatics Proceedings session.

Automated cellular annotation for high-resolution images of adult Caenorhabditis elegans by Sarah J. Aerni et al. [DOI]

Work uses C. elegans for studying development, where we can uniquely identy all 959 cells. The question is how to do so automatically as it takes hours/days to do so viusally.

Unlike previous work, they use morphological features of cells and not just expected location. They also allow for variable cell division. The result is higher accuracy in labeled data.

FuncISH: learning a functional representation of neural ISH images by Noa Liscovitch et al. [DOI]

(I blogged about this paper before)

This work looks at gene expression in the brain. Images are represented using local features. They do not use the scale invariance of the SIFT representation as the images are all at the same scale.

The genes are mapped to functional annotations, which is more effective than the previously published baselines, which only used the images. This can pick up similarity of genes that are expressed in different cell regions.

Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields by Iulian Pruteanu-Malinici et al. [DOI]

Work with in-situ hybridization images on Drosophila embryos across genes and time. Features were extracted using a sparse Bayesian factor model. Then, the temporal aspect of the data is modeled using a conditional random field, which improves results when compared to considering the inputs as independent.

A high-throughput framework to detect synapses in electron microscopy images by Saket Navlakha et al. [DOI]

Presentation of methodological advances in detecting synapses, involving both new laboratorial and new computational methods. The basic lab technique was a now-unused 50 year-old method. The most interesting aspect is that the experimental technique is justifiedbecause it makes (automatic) analysis easier.

They also tackled the typically ignore problem of generalizing a model learned on a particular set of samples to a new set of similar but not quite the same of samples. They empirically showed that Co-training works well for this problem if you are careful. Nice!

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One thought on “Bioimage Analysis Proceedings Session at #ISBMECCB (Live Blogging)

  1. Pingback: Mahotas software paper published | Meta Rabbit

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