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FCNN-based axon segmentation for convection-enhanced delivery optimization

  • Marco Vidotto
  • Elena De Momi
  • Michele Gazzara
  • Leonardo S. Mattos
  • Giancarlo Ferrigno
  • Sara MocciaEmail author
Original Article
  • 40 Downloads

Abstract

Purpose

Glioblastoma multiforme treatment is a challenging task in clinical oncology. Convection- enhanced delivery (CED) is showing encouraging but still suboptimal results due to drug leakages. Numerical models can predict drug distribution within the brain, but require retrieving brain physical properties, such as the axon diameter distribution (ADD), through axon architecture analysis. The goal of this work was to provide an automatic, accurate and fast method for axon segmentation in electronic microscopy images based on fully convolutional neural network (FCNN) as to allow automatic ADD computation.

Methods

The segmentation was performed using a residual FCNN inspired by U-Net and Resnet. The FCNN training was performed exploiting mini-batch gradient descent and the Adam optimizer. The Dice coefficient was chosen as loss function.

Results

The proposed segmentation method achieved results comparable with already existing methods for axon segmentation in terms of Information Theoretic Scoring (\(0.98\%\)) with a faster training (5 h on the deployed GPU) and without requiring heavy post-processing (testing time was 0.2 s with a non-optimized code). The ADDs computed from the segmented and ground-truth images were statistically equivalent.

Conclusions

The algorithm proposed in this work allowed fast and accurate axon segmentation and ADD computation, showing promising performance for brain microstructure analysis for CED delivery optimization.

Keywords

Axon segmentation Electron microscopy Deep learning Convection-enhanced delivery Glioblastoma 

Notes

Acknowledgements

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 688279.

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest to disclose.

Ethical standards

This article does not contain any studies with human participants. All applicable international, national and/or institutional guidelines for the care and use of animals were followed.

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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Department of Electronics, Information and Bioengineering (DEIB)Politecnico di MilanoMilanItaly
  2. 2.Department of Advanced Robotics (ADVR)Istituto Italiano di TecnologiaGenoaItaly
  3. 3.Department of Information Engineering (DII)Università Politecnica delle MarcheAnconaItaly

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