Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.

Journal: Arteriosclerosis, thrombosis, and vascular biology
Published Date: Oct 1, 2021

Abstract

Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that includes state-of-the-art deep learning models for atherosclerotic plaque segmentation. Approach and Results: Vesseg is a containerized, extensible, open-source, and user-oriented tool. It includes 2 models, trained and tested on 1089 hematoxylin-eosin stained mouse model atherosclerotic brachiocephalic artery sections. The models were compared to 3 human raters. Vesseg can be accessed at https://vesseg .online or downloaded. The models show mean Soerensen-Dice scores of 0.91+/-0.15 for plaque and 0.97+/-0.08 for lumen pixels. The mean accuracy is 0.98+/-0.05. Vesseg is already in active use, generating time savings of >10 minutes per slide. Conclusions: Vesseg brings state-of-the-art deep learning methods to atherosclerosis research, providing drastic time savings, while allowing for continuous improvement of models and the underlying pipeline.

Authors

  • Jacob M Murray
    AG Computational Radiology, Abteilung Radiologie, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland.
  • Phillip Pfeffer
    Department of Anesthesiology, University of Heidelberg, Germany (P.P., A.H., J.H., L.K., H.J., M.A.W., J.L.).
  • Robert Seifert
    Department of Nuclear Medicine, Medical Faculty, University Hospital Essen, Essen, Deutschland.
  • Alexander Hermann
    Department of Anesthesiology, University of Heidelberg, Germany (P.P., A.H., J.H., L.K., H.J., M.A.W., J.L.).
  • Jessica Handke
    Department of Anesthesiology, University of Heidelberg, Germany (P.P., A.H., J.H., L.K., H.J., M.A.W., J.L.).
  • Laura Kummer
    Department of Anesthesiology, University of Heidelberg, Germany (P.P., A.H., J.H., L.K., H.J., M.A.W., J.L.).
  • Henrike Janssen
    Department of Anesthesiology, University of Heidelberg, Germany (P.P., A.H., J.H., L.K., H.J., M.A.W., J.L.).
  • Markus A Weigand
    Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Heinz-Peter Schlemmer
    From the Department of Radiology (D.B., P.S., J.P.R., P.K., K.Y., M.F., H.P.S.), Division of Medical Image Computing (S.K., M.G., N.G., K.H.M.H.), Division of Statistics (M.W.), and Department of Medical Physics (T.A.K., F.D.), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany (D.B., H.P.S., K.H.M.H.); and Departments of Urology (J.P.R., B.H., M.H., B.A.H.) and Neuroradiology (P.K.), University of Heidelberg Medical Center, Heidelberg, Germany.
  • Jan Larmann
    Department of Anesthesiology, University of Heidelberg, Germany (P.P., A.H., J.H., L.K., H.J., M.A.W., J.L.).
  • Jens Kleesiek
    AG Computational Radiology, Abteilung Radiologie, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. j.kleesiek@dkfz-heidelberg.de.

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