An analysis of the influence of transfer learning when measuring the tortuosity of blood vessels.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 16, 2022

Abstract

BACKGROUND AND OBJECTIVE: Convolutional Neural Networks (CNNs) can provide excellent results regarding the segmentation of blood vessels. One important aspect of CNNs is that they can be trained on large amounts of data and then be made available, for instance, in image processing software. The pre-trained CNNs can then be easily applied in downstream blood vessel characterization tasks, such as the calculation of the length, tortuosity, or caliber of the blood vessels. Yet, it is still unclear if pre-trained CNNs can provide robust, unbiased, results in downstream tasks involving the morphological analysis of blood vessels. Here, we focus on measuring the tortuosity of blood vessels and investigate to which extent CNNs may provide biased tortuosity values even after fine-tuning the network to a new dataset under study.

Authors

  • Matheus V da Silva
    Department of Computer Science, Federal University of São Carlos, São Carlos, SP, Brazil.
  • Julie Ouellette
    Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada; Neuroscience Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Baptiste Lacoste
    Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
  • Cesar H Comin
    Department of Computer Science, Federal University of São Carlos, São Carlos, São Paulo, Brazil.

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