By only manipulating the basic element of DCNNs based on Gabor filters, i.e., the convolution operator, GCNs can be easily implemented and are compatible with any popular deep learning architecture. In this paper, we propose a new deep model, termed Gabor Convolutional Networks (GCNs or Gabor CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of deep learned features to the orientation and scale changes. However, such excellent properties have not been well explored in the popular deep convolutional neural networks (DCNNs). Steerable properties dominate the design of traditional filters, e.g., Gabor filters, and endow features the capability of dealing with spatial transformations. Using a multi-pathways network, segmentation results can be improved, and the probability map as input is robust against intensity changes in clinical data. We propose the first 3D liver vessel segmentation network using deep learning. The results demonstrate impressive performance in comparison with the state-of-the-art methods. To validate the effectiveness and efficiency of the proposed method, we conducted experiments with 20 data (public datasets) with different contrast levels and different device value ranges. The proposed deep network provides a vessel probability map for voxels in the target medical data, which is used in a post-process to generate the final segmentation result. Then, we extract vessels based on the proposed network, which is robust and sufficiently general to handle images with different contrast obtained by various imaging systems. Furthermore, due to the large variety of medical data device values, we transform a raw medical image into a probability map as input to the network. Thus, it is expected to provide a more reliable recognition performance by exploring the 3D structure. The proposed method trains a deep network for binary classification based on extracted training patches on three planes (sagittal, coronal, and transverse planes) centered on the focused voxels. We propose an automatic and robust vessel segmentation approach that uses a multi-pathways deep learning network. This is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image data. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.Įxtraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. The resulting model is fast to compute, converges more rapidly and requires fewer examples to achieve a given performance than more general techniques such as U-Net. A simple model containing a sequence of five such blocks was tested on CHASE-DB1 dataset, and achieved better performance comparing to the benchmark with only \(0.6\%\) of the parameters and \(25\%\) of the training examples. Invariance to scale can be added using a pyramid pooling layer. We use Gabor functions to guide the training of the kernels, and demonstrate that the resulting kernels generally form rotated versions of the same pattern. This introduces a degree of orientation invariance by construction. We show how two basis kernels can lead to the equivalent of eight orientations. In particular, by reflecting and rotating a single oriented kernel we can generate four versions at different angles. We introduce the Multi Angle and Scale Convolutional Unit (MASC) with a novel training approach called Response Shaping. We take advantage of this self-similarity by demonstrating a CNN based segmentation system that requires far fewer parameters than conventional approaches. Such structures often have no preferred direction and a range of possible scales. Many medical and biological applications involve analysing vessel-like structures.
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