Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence. Moreover, a two-stage training has been proposed as a countermeasure for the unbalanced nature of the dataset. The FCNN training was performed on 20 CE-CBCT volumes exploiting mini-batch gradient descent andthe Adam optimizer. Binary cross entropy was used as loss function. Performance evaluation was conducted considering 5 datasets. A median value of Dice, Precision and Recall of 0.79, 0.8 and 0.69 were obtained with respect to manual annotations.

Brain-vascular segmentation for SEEG planning via a 3D fully-convolutional neural network

Moccia, Sara;
2019-01-01

Abstract

Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence. Moreover, a two-stage training has been proposed as a countermeasure for the unbalanced nature of the dataset. The FCNN training was performed on 20 CE-CBCT volumes exploiting mini-batch gradient descent andthe Adam optimizer. Binary cross entropy was used as loss function. Performance evaluation was conducted considering 5 datasets. A median value of Dice, Precision and Recall of 0.79, 0.8 and 0.69 were obtained with respect to manual annotations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/828353
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