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Incorporating prior knowledge in medical image segmentation: a survey
Medical image segmentation, the task of partitioning an image into meani...
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Deep Semantic Segmentation of Natural and Medical Images: A Review
The (medical) image semantic segmentation task consists of classifying e...
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Learning Euler's Elastica Model for Medical Image Segmentation
Image segmentation is a fundamental topic in image processing and has be...
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Convexity Shape Constraints for Image Segmentation
Segmenting an image into multiple components is a central task in comput...
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3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation
Recent advancements in medical image segmentation techniques have achiev...
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Deep Convolutional Neural Networks with Spatial Regularization, Volume and Star-shape Priori for Image Segmentation
We use Deep Convolutional Neural Networks (DCNNs) for image segmentation...
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PAN: Projective Adversarial Network for Medical Image Segmentation
Adversarial learning has been proven to be effective for capturing long-...
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High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To mitigate this effect, recent research works have focused on incorporating spatial information or prior knowledge to enforce anatomically plausible segmentation. If the integration of prior knowledge in image segmentation is not a new topic in classical optimization approaches, it is today an increasing trend in CNN based image segmentation, as shown by the growing literature on the topic. In this survey, we focus on high level prior, embedded at the loss function level. We categorize the articles according to the nature of the prior: the object shape, size, topology, and the inter-regions constraints. We highlight strengths and limitations of current approaches, discuss the challenge related to the design and the integration of prior-based losses, and the optimization strategies, and draw future research directions.
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