Significados de image segmentation em inglês

We propose a simple strategy to improve automatic medical image segmentation.

However, deep learning is rapidly overtaking classical approaches for image segmentation.

Energy functional minimization is an increasingly popular technique for image segmentation.

PF within each hemithorax was quantified using a semiautomatic method of image segmentation.

Abdominal image segmentation is a challenging, yet important clinical problem.

This limitation has inspired recent work on few-shot and zero-shot learning for image segmentation.

We propose an image segmentation method based on texture analysis.

Our approach can be readily extended to higher-dimensional image segmentation.

Evolutionary schemes based on the level set theory are effective tools for medical image segmentation.

Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation.

Color image processing, a histogram-based function of Image J for image segmentation, was performed with color moments thresholding technique.

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations.

An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems.

Motivation: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models.

Directed acyclic graphs, such as trees, have been widely used to model top-down statistical dependences as a prior for improved image segmentation.

Combination of different image modalities is advantageous for image segmentation, quantitative trait derivation, and assessment of a more accurate and extended plant phenotype.