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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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A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation
Despite the tremendous success of deep neural networks in medical image ...
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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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Medical Image Segmentation Using Deep Learning: A Survey
Deep learning has been widely used for medical image segmentation and a ...
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Assessing the Role of Random Forests in Medical Image Segmentation
Neural networks represent a field of research that can quickly achieve v...
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Automatic Image Pixel Clustering based on Mussels Wandering Optimiz
Image segmentation as a clustering problem is to identify pixel groups o...
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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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