Marco Vidotto1, Elena De Momi1, Michele Gazzara1, Leonardo S Mattos2, Giancarlo Ferrigno1, Sara Moccia3,4. 1. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133, Milan, MI, Italy. 2. Department of Advanced Robotics (ADVR), Istituto Italiano di Tecnologia, Via Morego 30, 16136, Genoa, GE, Italy. 3. Department of Advanced Robotics (ADVR), Istituto Italiano di Tecnologia, Via Morego 30, 16136, Genoa, GE, Italy. s.moccia@univpm.it. 4. Department of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131, Ancona, AN, Italy. s.moccia@univpm.it.
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 ([Formula: see text]) 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.
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 ([Formula: see text]) 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.
Entities:
Keywords:
Axon segmentation; Convection-enhanced delivery; Deep learning; Electron microscopy; Glioblastoma
Authors: Marco Vidotto; Andrea Bernardini; Marco Trovatelli; Elena De Momi; Daniele Dini Journal: Proc Natl Acad Sci U S A Date: 2021-09-07 Impact factor: 11.205