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.

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections / E. Casiraghi, V. Huber, M. Frasca, M. Cossa, M. Tozzi, L. Rivoltini, B. Eugenio Leone, A. Villa, B. Vergani. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - 19:S10(2018 Oct 15), pp. 357.281-357.297. [10.1186/s12859-018-2302-3]

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

E. Casiraghi
Primo
;
M. Frasca;
2018-10-15

Abstract

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.
Comparative analysis; Digital image processing; Histochemical and immunohistochemical image analysis; Statistical analysis; Supervised learning methods; Structural Biology; Biochemistry; Molecular Biology; Computer Science Applications1707 Computer Vision and Pattern Recognition; Applied Mathematics
Settore INF/01 - Informatica
PIANO DI SOSTEGNO ALLA RICERCA 2015-2017 - LINEA 2 "DOTAZIONE ANNUALE PER ATTIVITA' ISTITUZIONALE"
Article (author)
File in questo prodotto:
File Dimensione Formato  
s12859-018-2302-3 (1).pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 7.64 MB
Formato Adobe PDF
7.64 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/596764
Citazioni
  • ???jsp.display-item.citation.pmc??? 6
  • Scopus 15
  • ???jsp.display-item.citation.isi??? 14
social impact