In recent years, many pre-processing filters have been developed in order to enhance anatomical structures on chest CT images. These filters are typically based on the analysis of the multiscale second-order local information of the image, that helps identify structures with even (tubes) or odd (surfaces) symmetries. Therefore, they often require specific parameter tuning to enhance the different structures. Moreover, while the filters seem to be able to isolate the structure of interest, they do not provide information about the sub-voxel location of the feature. In this work, we present a novel method for vessel, airway, and fissure strength computation on chest CT images using convolutional neural networks. A scale-space particle segmentation is used to isolate training points for vessels, airways, and fissures which are then used to train an 8-layer neural network with 3 convolutional layers which define high order local information of the image. The network returns a probability map of each feature and provides information on the feature offset from the voxel sampling center, allowing for sub-voxel location of the different structures. The proposed method has been evaluated on clinical CT images and compared to other methods for feature enhancement available in the literature. Results show that the proposed method outperforms competing algorithms in terms of enhancement and is also unique in providing subvoxel information.
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