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A New Adaptation Phase for Thresholds in a CBR System Associated to a Region Growing Algorithm to Segment Tumoral Kidneys

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Case-Based Reasoning Research and Development (ICCBR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12311))

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Abstract

Image segmentation is an abundant topic for computer vision and image processing. Most of the time, segmentation is not fully automated, and a user is required to guide the process in order to obtain correct results. Yet, even with programs, it is a time-consuming process. In a medical context, segmentation can provide a lot of information to surgeons, but since this task is manual, it is rarely executed because of time. Artificial Intelligence (AI) is a powerful approach to create viable solutions for automated treatments. In this paper, we reused a case-based reasoning (CBR) system previously developed to segment renal parenchyma with a region growing algorithm and we completed its adaptation phase allowing a better adjustment of parameters before segmentation. Compared to the previous system, we added an adaptation for the thresholds values in addition to the adaptation of the seeds coordinates. We compared several versions of our new adaptation in order to determine the best and we confronted it with a deep learning approach realized in similar conditions.

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Acknowledgments

The authors wish to thank Pr Frédéric Auber, Dr Marion Lenoir-Auber and Dr Yann Chaussy of the Centre Hospitalier Régional Universitaire de Besançon for their expertise with nephroblastoma and for achieving the manual segmentations with help of Loredane Vieille. The authors thanks European Community (European FEDER) for financing this work by the INTERREG V, the Communauté d’Agglomération du Grand Besançon and the Cancéropôle Grand-Est.

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Authors and Affiliations

  1. FEMTO-ST Institute, University Bourgogne-Franche-Comté, CNRS, DISC, 16 route de Gray, 25030, Besançon, France

    Florent Marie, Julien Henriet & Jean-Christophe Lapayre

Authors
  1. Florent Marie
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  2. Julien Henriet
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  3. Jean-Christophe Lapayre
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Correspondence to Florent Marie .

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Editors and Affiliations

  1. School of Computer Science, University of Auckland, Auckland, New Zealand

    Ian Watson

  2. College of Computing and Informatics, Drexel University, Philadelphia, PA, USA

    Rosina Weber

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Marie, F., Henriet, J., Lapayre, JC. (2020). A New Adaptation Phase for Thresholds in a CBR System Associated to a Region Growing Algorithm to Segment Tumoral Kidneys. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-58342-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58341-5

  • Online ISBN: 978-3-030-58342-2

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