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Robustness of Radiomics Features to Varying Segmentation Algorithms in Magnetic Resonance Images

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Image Analysis and Processing. ICIAP 2022 Workshops (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13373))

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Abstract

Aim: To verify the accuracy of different segmentation algorithms applied on a dataset of 50 patients suffering from enlargement of the median lobe of the prostate district, to establish whether it is possible to support the work of medical physicians in radiomics analyses through semi-automatic segmentation approaches.

Materials and Methods: Seven algorithms were used for prostate segmentation in MR images and for the subsequent extraction of radiomics features. A statistical analysis was carried out considering the features extracted from semi-automatic and manual segmentations. The analysis was based on the ANOVA test, followed by the Tukey test to verify the repeatability of the algorithms, and on the calculation of the intraclass correlation coefficient to verify the reliability and robustness of the extracted features. Based on the correlation between the binary masks extracted for each algorithm and the corresponding binary mask of the medical physicians’ segmentation, a volumetric analysis was conducted.

Results: The best semi-automatic algorithm to support the medical physician among those evaluated is the “Fill between slices” algorithm, which is also the fastest of all. The least reliable algorithms are those based on the similarity of grey levels.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Sapienza-University of Rome, 00184, Rome, Italy

    Luca Cairone, Franco Marinozzi & Fabiano Bini

  2. Ri.MED Foundation, Via Bandiera 11, 90133, Palermo, Italy

    Viviana Benfante & Albert Comelli

  3. Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo, 90127, Palermo, Italy

    Viviana Benfante & Antonino Tuttolomondo

  4. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015, Cefalù, Italy

    Viviana Benfante

  5. Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Samuel Bignardi & Anthony Yezzi

  6. Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90100, Palermo, Italy

    Giuseppe Salvaggio

Authors
  1. Luca Cairone
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  2. Viviana Benfante
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  3. Samuel Bignardi
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  4. Franco Marinozzi
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  5. Anthony Yezzi
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  6. Antonino Tuttolomondo
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  7. Giuseppe Salvaggio
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  8. Fabiano Bini
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  9. Albert Comelli
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Corresponding author

Correspondence to Albert Comelli .

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Editors and Affiliations

  1. National Research Council, Lecce, Italy

    Pier Luigi Mazzeo

  2. Università Politecnica delle Marche, Ancona, Italy

    Emanuele Frontoni

  3. Boston University, Boston, MA, USA

    Stan Sclaroff

  4. National Research Council, Lecce, Italy

    Cosimo Distante

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Cairone, L. et al. (2022). Robustness of Radiomics Features to Varying Segmentation Algorithms in Magnetic Resonance Images. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_41

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  • DOI: https://doi.org/10.1007/978-3-031-13321-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13320-6

  • Online ISBN: 978-3-031-13321-3

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