Abstract
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Highlights
Image segmentation techniques aim to partition an image into meaningful parts which are used for further analysis
We demonstrate that the lower dimensional representations learnt by the proposed anatomically constrained neural networks (ACNN) can be useful for classification of pathologies such as dilated and hypertrophic cardiomyopathy, and it does not require point-wise correspondence search between subjects as in [39]
C c=1 i∈S log ef(c,i) j ef(j,i) maps correspond to log likelihood values. These feature technique that extends previous work [34] and that is robust against slice misalignments; our approach is computationally more efficient than the state-of-the-art SR-convolutional neural network (CNN) model [34] as the feature extraction is performed in the low-dimensional image space. (IV) Last, we demonstrate state-of-the-art 3D-US cardiac segmentation results on the CETUS’14 Benchmark
Summary
Image segmentation techniques aim to partition an image into meaningful parts which are used for further analysis. Besides variational models, cascaded convolutional architectures [27], [37] have been shown to discover priors on shape and structure in label space without any a priori specification. This comes at the cost of increased model complexity and computational needs. We present a novel and generic way to incorporate global shape/label information into NNs. The proposed approach, namely anatomically constrained neural networks (ACNN), is mainly motivated by the early work on shape priors and image segmentation, in particular PCA based statistical [13] and active shape models [14]. In the context of medical imaging, our priors enforce the synthesised HR images to be anatomically meaningful while minimising a traditional image reconstruction loss function
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