PorMe: A validated open-source image-based pore size and porosity measurement tool for 3D-printed structures

The paper presents a non-destructive image analysis tool for characterizing in situ the inner micro-architectural features of 3D-printed scaffolds for tissue engineering applications. The study aims to provide a high throughput method for assessing geometrical porous and fiber-based properties such as fiber diameter, orientation, pore size, and porosity. Through this study, an open-source image-based software has been created that uses images acquired in each vertical plane during the printing process. The images are automatically added one above the other and thus a series of three-dimensional geometric properties of the printed structures are analyzed. The method was validated by using 3D-cilindrical scaffolds of 10 mm diameter and 17 mm height, with pore size around 400 µm and porosity range of 40-60 %. It was demonstrated a high porosity prediction and homogeneous inner architecture between our current method, Archimedes-derived porosity, and a high standard microCT-based 3D analysis. This work also details the steps of the porosity measurement (PorMe) algorithm, which involves image acquisition, segmentation, invalid pore determination, pore size calculation, and porosity determination in a high-throughput and non-destructive manner. In situ, non-destructive image analysis tool to assess scaffold geometrical properties Layer-wise assessment of pore size, fiber diameter and orientation as opposed to just outer (top and bottom) layers Open-source tools used to develop the image analysis tool, to be made available for the research community.

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Journal
MethodsX
Published
2025-09-12
DOI
https://doi.org/10.1016/j.mex.2025.103611
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
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article

PorMe: A validated open-source image-based pore size and porosity measurement tool for 3D-printed structures

Juan F. Vivanco, José I. Contreras Raggio, Bernhard Weisse, Miguel Pardo et al.
MethodsX
Additive Manufacturing and 3D Printing Technologies
article

PorMe: A validated open-source image-based pore size and porosity measurement tool for 3D-printed structures

Juan F. Vivanco, José I. Contreras Raggio, Bernhard Weisse, Miguel Pardo, Ameet K. Aiyangar
article en

Abstract

The paper presents a non-destructive image analysis tool for characterizing in situ the inner micro-architectural features of 3D-printed scaffolds for tissue engineering applications. The study aims to provide a high throughput method for assessing geometrical porous and fiber-based properties such as fiber diameter, orientation, pore size, and porosity. Through this study, an open-source image-based software has been created that uses images acquired in each vertical plane during the printing process. The images are automatically added one above the other and thus a series of three-dimensional geometric properties of the printed structures are analyzed. The method was validated by using 3D-cilindrical scaffolds of 10 mm diameter and 17 mm height, with pore size around 400 µm and porosity range of 40-60 %. It was demonstrated a high porosity prediction and homogeneous inner architecture between our current method, Archimedes-derived porosity, and a high standard microCT-based 3D analysis. This work also details the steps of the porosity measurement (PorMe) algorithm, which involves image acquisition, segmentation, invalid pore determination, pore size calculation, and porosity determination in a high-throughput and non-destructive manner. In situ, non-destructive image analysis tool to assess scaffold geometrical properties Layer-wise assessment of pore size, fiber diameter and orientation as opposed to just outer (top and bottom) layers Open-source tools used to develop the image analysis tool, to be made available for the research community.

MethodsXVol. 15
University of Bern (CH), Adolfo Ibáñez University (CL), University Hospital of Bern (CH), Swiss Federal Laboratories for Materials Science and Technology (CH)
Openalex Percentile: Top 70%
Additive Manufacturing and 3D Printing Technologies
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