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Evaluating Deep Learning Methods for Identifying Nuclei


A notable development in the field of fluorescent imaging analysis has been made recently with improvements in segmentation. The optimal approach to segmentation, which refers to the process of delimiting the boundaries of objects, is through solutions offered by deep learning solutions.

 

This a subset of machine learning involves artificial neural networks, algorithms inspired by the human brain, that ‘learn’ from repeated interrogation of large quantities of data. A team from X has constructed a framework f evaluation to evaluate improvements in nucleus segmentation seen when adopting deep learning methods.

Traditionally, pixel-overlap is used for nucleus/cell segmentation evaluation but missing and merged objects are not diagnosed earlier in the segmentation process. The team sought to determine an evaluation method that correctly differentiates between true positives and errors.

 

Therefore, classical machine learning and image processing algorithms do not satisfactorily capture biologically important error modes. Because of this, cell and nuclei segmentation algorithms are difficult to assess.ow

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