13 March 2017 Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks
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Abstract
Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.
Conference Presentation
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Chaoen Hu, Chaoen Hu, Hui Hui, Hui Hui, Shuo Wang, Shuo Wang, Di Dong, Di Dong, Xia Liu, Xia Liu, Xin Yang, Xin Yang, Jie Tian, Jie Tian, } "Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101370K (13 March 2017); doi: 10.1117/12.2254714; https://doi.org/10.1117/12.2254714
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