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Using the polar transform for efficient deep learning-based aorta segmentation in CTA images
Get PDFMedical image segmentation often requires segmenting multiple elliptical objects on a single image. This includes, among other tasks, segmenting vessels such as the aorta in axial CTA slices. In this paper, we present a general approach to improving the semantic segmentation performance of neural networks in these tasks and validate our approach on the task of aorta segmentation. We use a cascade of two neural networks, where one performs a rough segmentation based on the U-Net architecture and the other performs the final segmentation on polar image transformations of the input. Connected component analysis of the rough segmentation is used to construct the polar transformations, and predictions on multiple transformations of the same image are fused using hysteresis thresholding. We show that this method improves aorta segmentation performance without requiring complex neural network architectures. In addition, we show that our approach improves robustness and pixel-level recall while achieving segmentation performance in line with the state of the art
3D U-Net based method for fast segmentation of whole heart from CT images
No full textThere is an increasing number of clinical applications where deep learning plays an important role. Heart chamber segmentation enables delineation of anatomical structures of heart and it is a prerequisite for a wide range of clinical applications. 3D U-Net architecture consistently achieves the highest scores in various medical imaging challenges. NoNewNet architecture is a modification of the 3D U-Net architecture which was shown to outperform the original 3D U-Net and was recently implemented inside the NiftyNet package. In this paper we demonstrate that with the properly trained NoNewNet network and NiftyNet we can outperform the current state-of-the-art networks. The evaluation of the trained network was performed on 20 3D CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset using five-fold cross-validation. We experimentally prove that border size can significantly reduce inference time without affecting segmentation accuracy. Additionally, we provide the discussion of the effects of some of the NiftyNet configuration parameters on the performance of the network
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