DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images

Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applications, especially for graph-based or seed-based reconstruction methods and can help to visualize the morphology structures. There are a few hand-crafted models proposed to detect the branch points. However, they are highly dependent on the empirical setting of the parameters for different images. In this paper, we propose a DeepBranch model for branch point detection with two-level designed convolutional networks, a candidate region segmenter and a false positive reducer. On the first level, an improved 3D U-Net model with anisotropic convolution kernels is employed to detect initial candidates. Compared with the traditional sliding window strategy, the improved 3D U-Net can avoid massive redundant computations and dramatically speed up the detection process by employing dense-inference with fully convolutional neural networks (FCN). On the second level, a method based on multi-scale multi-view convolutional neural networks (MSMV-Net) is proposed for false positive reduction by feeding multi-scale views of 3D volumes into multiple streams of 2D convolution neural networks (CNNs), which can take full advantage of spatial contextual information as well as fit different sizes. Experiments on multiple 3D biomedical images of neurons, retinal blood vessels and bronchi confirm that the proposed 3D branch point detection method outperforms other state-of-the-art detection methods, and is helpful for graph-based or seed-based reconstruction methods.