TY - JOUR
T1 - A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections
AU - Casiraghi, Elena
AU - Huber, Veronica
AU - Frasca, Marco
AU - Cossa, Mara
AU - Tozzi, Matteo
AU - Rivoltini, Licia
AU - Leone, Biagio Eugenio
AU - Villa, Antonello
AU - Vergani, Barbara
PY - 2018/10/15
Y1 - 2018/10/15
N2 - BACKGROUND: In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension.SOFTWARE: The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers' relative location). Additionally, it computes novel measures of markers' co-existence in tissue volumes depending on their density.CONCLUSIONS: Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.
AB - BACKGROUND: In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension.SOFTWARE: The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers' relative location). Additionally, it computes novel measures of markers' co-existence in tissue volumes depending on their density.CONCLUSIONS: Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.
KW - Algorithms
KW - Biomarkers, Tumor/metabolism
KW - Computational Biology/methods
KW - Decision Trees
KW - Humans
KW - Image Processing, Computer-Assisted/methods
KW - Immunohistochemistry
KW - Software
KW - Staining and Labeling
KW - Support Vector Machine
U2 - 10.1186/s12859-018-2302-3
DO - 10.1186/s12859-018-2302-3
M3 - Article
C2 - 30367588
SN - 1471-2105
VL - 19
SP - 357
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - Suppl 10
ER -