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Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions

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Book cover Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12262))

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Abstract

Diagnosis of various pancreatic lesions in CT images is a challenging task owing to a significant overlap in their imaging appearance. An accurate diagnosis of pancreatic lesions and the assessment of their malignant progression, or the grade of dysplasia, is crucial for optimal patient management. Typically, the grade of dysplasia is confirmed histologically via biopsy, yet certain radiological findings, including extrapancreatic, can serve as diagnostic clues of the disease progression. This work introduces a novel method of transforming intermediate activations for processing intact imaging data of varying sizes with convnets with linear layers. Our method allows to efficiently leverage the 3D information of the entire abdominal CT scan to acquire a holistic picture of all radiological findings for an improved and more precise classification of pancreatic lesions. Our model outperforms current state-of-the-art methods in classifying four most common lesion types (by 2.92%), while additionally diagnosing the grade of dysplasia. We conduct a set of experiments to illustrate the effects of a holistic CT analysis and the auxiliary diagnostic data on the accuracy of the final diagnosis.

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Acknowledgments

This research was supported in part by NSF grants NRT1633299, CNS1650499, OAC1919752, and ICER1940302.

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Authors and Affiliations

  1. Department of Computer Science, Stony Brook University, Stony Brook, NY, USA

    Konstantin Dmitriev & Arie E. Kaufman

Authors
  1. Konstantin Dmitriev
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  2. Arie E. Kaufman
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Corresponding author

Correspondence to Konstantin Dmitriev .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Leo Joskowicz

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Dmitriev, K., Kaufman, A.E. (2020). Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_28

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  • DOI: https://doi.org/10.1007/978-3-030-59713-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59712-2

  • Online ISBN: 978-3-030-59713-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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