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A Framework for Fusion of T1-Weighted and Dynamic MRI Sequences

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Book cover Image Analysis and Recognition (ICIAR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12132))

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Abstract

Breast cancer imaging research has seen continuous progress throughout the years. Innovative visualization tools and easier planning techniques are being developed. Image segmentation methodologies generally have best results when applied to specific types of exams or sequences, as their features enhance and expedite those approaches. Particular methods have more purchase with the segmentation of particular structures. This is the case with diverse breast structures and the respective lesions on MRI sequences, over T1w and Dyn.

The present study presents a methodology to tackle an unapproached task. We aim to facilitate the volumetric alignment of data retrieved from T1w and Dyn sequences, leveraging breast surface segmentation and registration. The proposed method revolves around Canny edge detection and mending potential holes on the surface, in order to accurately reproduce the breast shape. The contour is refined with a Level-set approach and the surfaces are aligned together using a restriction of the Iterative Closest Point (ICP) method. This could easily be applied to other paired same-time, volumetric sequences.

The process seems to have promising results as average two-dimensional contour distances are at sub-voxel resolution and visual results seem well within range for the valid transference of other segmented or annotated structures.

This work was funded by the ERDF - European Regional Development Fund through the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement and through the Portuguese National Innovation Agency (ANI) as a part of project BCCT.Plan–NORTE-01-0247-FEDER-01768 and by Fundação para a Ciência e a Tecnologia (FCT) within PhD grants number SFRH/BD/135834/2018 and SFRH/BD/115616/2016.

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Notes

  1. 1.

    BCCT.plan - 3D tool for planning breast cancer conservative treatment - NORTE-01-0247-FEDER-017688.

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Authors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    João F. Teixeira

  2. INESC TEC, Porto, Portugal

    João F. Teixeira, Sí­lvia Bessa & Hélder P. Oliveira

  3. Breast Unit, Champalimaud Clinical Centre, Champalimaud Foundation, Lisbon, Portugal

    Pedro F. Gouveia

  4. Faculty of Medical, Lisbon University, Lisbon, Portugal

    Pedro F. Gouveia

  5. Faculty of Sciences, University of Porto, Porto, Portugal

    Hélder P. Oliveira

Authors
  1. João F. Teixeira
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  2. Sí­lvia Bessa
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  3. Pedro F. Gouveia
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  4. Hélder P. Oliveira
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Corresponding author

Correspondence to João F. Teixeira .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

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Teixeira, J.F., Bessa, S., Gouveia, P.F., Oliveira, H.P. (2020). A Framework for Fusion of T1-Weighted and Dynamic MRI Sequences. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-50516-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50515-8

  • Online ISBN: 978-3-030-50516-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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