Abstract
Cerebrovascular diseases are one of the main causes of death and disability in the world. Within this context, fast and accurate automatic cerebrovascular segmentation is important for clinicians and researchers to analyze the vessels of the brain, determine criteria of normality, and identify and study cerebrovascular diseases. Nevertheless, automatic segmentation is challenging due to the complex shape, inhomogeneous intensity, and inter-person variability of normal and malformed vessels. In this paper, a deep convolutional neural network (CNN) architecture is used to automatically segment the vessels of the brain in time-of-flight magnetic resonance angiography (TOF MRA) images of healthy subjects. Bi-dimensional manually annotated image patches are extracted in the axial, coronal, and sagittal directions and used as input for training the CNN. For segmentation, each voxel is individually analyzed using the trained CNN by considering the intensity values of neighboring voxels that belong to its patch. Experiments were performed with TOF MRA images of five healthy subjects, using varying numbers of images to train the CNN. Cross validations revealed that the proposed framework is able to segment the vessels with an average Dice coefficient ranging from 0.764 to 0.786 depending on the number of images used for training. In conclusion, the results of this work suggest that CNNs can be used to segment cerebrovascular structures with an accuracy similar to other high-level segmentation methods.
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Acknowledgement
This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC). Dr. Alexandre X. Falcão and MSc. Alan Peixinho thank CNPq 302970/2014-2 and FAPESP 2014/12236-1.
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Phellan, R., Peixinho, A., Falcão, A., Forkert, N.D. (2017). Vascular Segmentation in TOF MRA Images of the Brain Using a Deep Convolutional Neural Network. In: Cardoso, M., et al. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS STENT CVII 2017 2017 2017. Lecture Notes in Computer Science(), vol 10552. Springer, Cham. https://doi.org/10.1007/978-3-319-67534-3_5
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