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Grouped multi-scale vision transformer for medical image segmentation. [PDF]
Sci RepJi Z, Chen Z, Ma X.
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Correction: ES-UNet: efficient 3D medical image segmentation with enhanced skip connections in 3D UNet. [PDF]
BMC Med ImagingPark M, Oh S, Park J, Jeong T, Yu S.
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Medical image segmentation by combining feature enhancement Swin Transformer and UperNet. [PDF]
Sci RepZhang L, Yin X, Liu X, Liu Z.
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Segmentation of medical images
Image and Vision Computing, 1993Abstract Segmentation and labelling remains the weakest step in many medical vision applications. This paper illustrates an approach based on generic modules which are designed to solve typical problems encountered in various applications, and which are controllable through adaptation of their parameters.
Rudi, Deklerck +2 more
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Pairwise learning for medical image segmentation
Medical Image Analysis, 2021Fully convolutional networks (FCNs) trained with abundant labeled data have been proven to be a powerful and efficient solution for medical image segmentation. However, FCNs often fail to achieve satisfactory results due to the lack of labelled data and significant variability of appearance in medical imaging.
Renzhen, Wang +4 more
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Advanced Materials Research, 2013
Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms.
Bing Song He, Feng Zhu, Yong Gang Shi
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Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms.
Bing Song He, Feng Zhu, Yong Gang Shi
openaire +1 more source
Loss odyssey in medical image segmentation
Medical Image Analysis, 2021The loss function is an important component in deep learning-based segmentation methods. Over the past five years, many loss functions have been proposed for various segmentation tasks. However, a systematic study of the utility of these loss functions is missing.
Jun Ma +7 more
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PIMedSeg: Progressive interactive medical image segmentation
Computer Methods and Programs in Biomedicine, 2023Accurate object segmentation in medical images is a crucial step in medical diagnosis and other applications. Despite years of research on automatic segmentation approaches, achieving clinically acceptable image quality remains challenging. Interactive segmentation is seen as a promising alternative; thus, we propose a new interactive segmentation ...
Xun, Gong +4 more
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2011
This chapter presents a new and efficient unsupervised color segmentation scheme named GBOD to detect visual objects from medical color images and to extract their color and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis.
Liana Stanescu +3 more
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This chapter presents a new and efficient unsupervised color segmentation scheme named GBOD to detect visual objects from medical color images and to extract their color and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis.
Liana Stanescu +3 more
openaire +1 more source