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Automatic tumor segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because regular CNNs can only exploit translation invariance, ignoring further inherent symmetries existing in medical images such as rotations and reflections. To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations. First, kernel-based equivariant operations are devised on every orientation, which can effectively address the gaps of learning symmetries in existing approaches. Then, to keep segmentation networks globally equivariant, we design distinctive group layers with layerwise symmetry constraints. By exploiting further symmetries, novel segmentation CNNs can dramatically reduce the sample complexity and the redundancy of filters (by roughly 2/3) over regular CNNs. More importantly, based on our novel framework, we show that a newly built GER-UNet outperforms its regular CNN-based counterpart and the state-of-the-art segmentation methods on real-world clinical data. Specifically, the group layers of our segmentation framework can be seamlessly integrated into any popular CNN-based segmentation architectures.
We propose a novel approach to image segmentation based on combining implicit spline representations with deep convolutional neural networks. This is done by predicting the control points of a bivariate spline function whose zero-set represents the s
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation
In recent years, deep learning based methods have shown success in essential medical image analysis tasks such as segmentation. Post-processing and refining the results of segmentation is a common practice to decrease the misclassifications originati
Accurate image segmentation is crucial for medical imaging applications. The prevailing deep learning approaches typically rely on very large training datasets with high-quality manual annotations, which are often not available in medical imaging. We
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less out