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A Hands-On Introduction to Spatial Model Checking Using VoxLogicA

– Invited Contribution

  • Conference paper
  • First Online:
Model Checking Software (SPIN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12864))

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Abstract

This paper provides a tutorial-style introduction, and a guide, to the recent advancements in spatial model checking that have made some relevant results possible. Among these, we mention fully automated segmentation of regions of interest in medical images by short, unambiguous spatial-logical specifications. This tutorial is aimed both at domain experts in medical imaging who would like to learn simple (scripting-alike) techniques for image analysis, making use of a modern, declarative language, and at experts in Formal Methods in Computer Science and Model Checking who would like to grasp how the theory of Spatial Logic and Model Checking has been turned into logic-based, dataset-oriented imaging techniques.

Research partially supported by the MIUR PRIN 2017FTXR7S “IT-MaTTerS”.

This tutorial is meant to complement the invited talk in the 27th International SPIN Symposium on Model Checking of Software by Vincenzo Ciancia, therefore listed as the first author. All the authors equally contributed to developments of the presented research line and are primary authors of this paper.

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Notes

  1. 1.

    See https://simpleitk.org/.

  2. 2.

    Indeed, it is not the intention of the research line around VoxLogicA to compete against machine-learning based approaches. Rather, we expect that the two can be complementary: VoxLogicA specifications can certainly be used to coordinate various machine-learning based steps in order to obtain procedures that have a degree of machine-learning based operation, but are still modifiable and explainable.

  3. 3.

    We use VoxLogicA version 1.0. It can be downloaded from https://github.com/vincenzoml/VoxLogicA. The example images and files of this paper are available at the same web site.

  4. 4.

    A VoxLogicA expression may return either an image – which can be Boolean-valued, number-valued, or have multiple number-valued channels – or a single value, which can be either a number or a Boolean value. No distinction is made between integer and floating point numbers; all numbers are treated as floating point internally.

  5. 5.

    In this respect, we consider our work still as the beginning of a research line, and we cannot predict if, for instance, novel logical operators will enable the development of very general specifications that operate on inhomogeneous domains.

  6. 6.

    Fluid-attenuated Inversion Recovery. See e.g. Wikipedia contributors, “Fluid-attenuated inversion recovery,” Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Fluid-attenuated_inversion_recovery.

  7. 7.

    This is a variant of the \(\rho \) operator used in [8], the difference being that with \(\rho \), the extremes of the path do not need to satisfy a.

  8. 8.

    The reader should now pause, and understand (even by experimenting) why actually, two reachability properties are needed.

  9. 9.

    In order to avoid issues related to overflow and low precision of 8-bit integers altogether, VoxLogicA can save images in the nifti format. Such format can use floating point values in pixels (and can also represent multi-dimensional images, for instance 3D MRI or CAT medical images). See https://nifti.nimh.nih.gov/.

  10. 10.

    We do not normalise the result to the maximum representable value 255, but just to 200, to make the lighter areas grey, which is more prominent on white paper.

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Acknowledgements

The authors wish to explicitly thank the current collaborators Nick Bezhanisvili, Giovanna Broccia, Laura Bussi, David Gabelaia, Fabio Gadducci, Gianluca Grilletti, Erik de Vink, and the coordinator of the Formal Methods and Tools laboratory of ISTI-CNR Maurice ter Beek, for their continuative support in turning the early developments of Spatial Model Checking, and the more recent work on VoxLogicA, into a solid research line, with several promising ongoing developments. The authors gratefully thank the Program Committee members of the 27th International SPIN Symposium on Model Checking of Software (PC chairs Alfons Laarman and Ana Sokolova) for giving us the opportunity to disseminate our results to such an amazing audience.

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Authors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Vincenzo Ciancia, Diego Latella & Mieke Massink

  2. Azienda Toscana Nord Ovest, S. C. Fisica Sanitaria Nord, Lucca, Italy

    Gina Belmonte

Authors
  1. Vincenzo Ciancia
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  2. Gina Belmonte
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  4. Mieke Massink
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Correspondence to Vincenzo Ciancia .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Alfons Laarman

  2. University of Salzburg, Salzburg, Austria

    Ana Sokolova

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Ciancia, V., Belmonte, G., Latella, D., Massink, M. (2021). A Hands-On Introduction to Spatial Model Checking Using VoxLogicA. In: Laarman, A., Sokolova, A. (eds) Model Checking Software. SPIN 2021. Lecture Notes in Computer Science(), vol 12864. Springer, Cham. https://doi.org/10.1007/978-3-030-84629-9_2

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