AI Article Synopsis
- Microscopic images of tissue sections help diagnose and monitor diseases by analyzing protein patterns related to those diseases.
- The study focuses on cholestatic liver diseases that involve the movement of transport proteins, highlighting the need for better diagnostic methods to quantify this translocation accurately.
- A new automated image analysis workflow has been developed to replace slow and subjective manual methods, using scale-free descriptors that outperform previous statistical techniques for quantifying protein translocation.
Article Abstract
Microscopic images of tissue sections are used for diagnosis and monitoring of therapy, by analysis of protein patterns correlating to disease states. Spatial protein distribution is influenced by protein translocation between different membrane compartments and quantified by comparison of microscopic images of biological samples. Cholestatic liver diseases are characterized by translocation of transport proteins, and quantification of their dislocation offers new diagnostic options. However, reliable and unbiased tools are lacking. The nowadays used manual method is slow, subjective and error-prone. We have developed a new workflow based on automated image analysis and improved it by the introduction of scale-free descriptors for the translocation quantification. This fast and unbiased method can substitute the manual analysis, and the suggested descriptors perform better than the earlier used statistical variance.
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| http://dx.doi.org/10.1109/IEMBS.2011.6091627 | DOI Listing |
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