Most of the time, the deep analysis of a biological sample requires the acquisition of images at different time points, using different modalities and/or different stainings. This information gives morphological, functional, and physiological insights, but the acquired images must be aligned to be able to proceed with the co-localisation analysis. Practically speaking, according to Aristotle’s principle, “The whole is greater than the sum of its parts”, multi-modal image registration is a challenging task that involves fusing complementary signals. In the past few years, several methods for image registration have been described in the literature, but unfortunately, there is not one method that works for all applications. In addition, there is currently no user-friendly solution for aligning images that does not require any computer skills. In this work, DS4H Image Alignment (DS4H-IA), an open-source ImageJ/Fiji plugin for aligning multimodality, immunohistochemistry (IHC), and/or immunofluorescence (IF) 2D microscopy images, designed with the goal of being extremely easy to use, is described. All of the available solutions for aligning 2D microscopy images have also been revised. The DS4H-IA source code; standalone applications for MAC, Linux, and Windows; video tutorials; manual documentation; and sample datasets are publicly available.

Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy Images / Piccinini, Filippo; Tazzari, Marcella; Tumedei, Maria Maddalena; Stellato, Mariachiara; Remondini, Daniel; Giampieri, Enrico; Martinelli, Giovanni; Castellani, Gastone; Carbonaro, Antonella. - In: SENSORS. - ISSN 1424-8220. - ELETTRONICO. - 24:2(2024), pp. 451.1-451.20. [10.3390/s24020451]

Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy Images

Piccinini, Filippo
Primo
;
Tumedei, Maria Maddalena;Stellato, Mariachiara;Remondini, Daniel;Giampieri, Enrico;Martinelli, Giovanni;Castellani, Gastone;Carbonaro, Antonella
Ultimo
2024

Abstract

Most of the time, the deep analysis of a biological sample requires the acquisition of images at different time points, using different modalities and/or different stainings. This information gives morphological, functional, and physiological insights, but the acquired images must be aligned to be able to proceed with the co-localisation analysis. Practically speaking, according to Aristotle’s principle, “The whole is greater than the sum of its parts”, multi-modal image registration is a challenging task that involves fusing complementary signals. In the past few years, several methods for image registration have been described in the literature, but unfortunately, there is not one method that works for all applications. In addition, there is currently no user-friendly solution for aligning images that does not require any computer skills. In this work, DS4H Image Alignment (DS4H-IA), an open-source ImageJ/Fiji plugin for aligning multimodality, immunohistochemistry (IHC), and/or immunofluorescence (IF) 2D microscopy images, designed with the goal of being extremely easy to use, is described. All of the available solutions for aligning 2D microscopy images have also been revised. The DS4H-IA source code; standalone applications for MAC, Linux, and Windows; video tutorials; manual documentation; and sample datasets are publicly available.
2024
Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy Images / Piccinini, Filippo; Tazzari, Marcella; Tumedei, Maria Maddalena; Stellato, Mariachiara; Remondini, Daniel; Giampieri, Enrico; Martinelli, Giovanni; Castellani, Gastone; Carbonaro, Antonella. - In: SENSORS. - ISSN 1424-8220. - ELETTRONICO. - 24:2(2024), pp. 451.1-451.20. [10.3390/s24020451]
Piccinini, Filippo; Tazzari, Marcella; Tumedei, Maria Maddalena; Stellato, Mariachiara; Remondini, Daniel; Giampieri, Enrico; Martinelli, Giovanni; Castellani, Gastone; Carbonaro, Antonella
File in questo prodotto:
File Dimensione Formato  
0077_2024_Sensors_Piccinini.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 9.86 MB
Formato Adobe PDF
9.86 MB Adobe PDF Visualizza/Apri

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: https://hdl.handle.net/11585/952774
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact