Results 241 to 250 of about 17,459,905 (291)

A frequency-spatial dual perception network for efficient and accurate medical image segmentation. [PDF]

Sci Rep
Chen D, Wu J, Zhang XY, Wang DH.
europepmc   +1 more source

Universal medical image segmentation via in-context cross-attention. [PDF]

Front Artif Intell
Ciusdel CF, Serban A, Passerini T.
europepmc   +1 more source

SEFormer for medical image segmentation with integrated global and local features. [PDF]

Sci Rep
Ge C, Pan H, Song Y, Zhang X, Zhou Z.
europepmc   +1 more source

MSCF-Net: A Vision Mamba Network with Multi-Scale Context Bridging and Cross-Layer Adaptive Fusion for Medical Image Segmentation. [PDF]

J Imaging
Guo J, Chen T, Hu J, Zhou Y.
europepmc   +1 more source

MFR-UNet: A Medical Image Segmentation Network With Fused Multi-Scale Feature Refinement. [PDF]

IET Syst Biol
Wang S, Shi G, Sun S, Wang Y, Zhang Y, Li W, Zhao Y, Cheng X.   +7 more
europepmc   +1 more source

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Segmentation of medical images

Image and Vision Computing, 1993
Abstract 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, Jan Cornelis 0001, Michel Bister   +2 more
openaire   +3 more sources

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

IEEE Transactions on Medical Imaging, 2019
Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation ...
Jiang Liu, Zaiwang Gu, Kang Zhou
exaly   +2 more sources

Interaction in the Segmentation of Medical Images: A Survey

Medical Image Analysis, 2001
Segmentation of the object of interest is a difficult step in the analysis of digital images. Fully automatic methods sometimes fail, producing incorrect results and requiring the intervention of a human operator. This is often true in medical applications, where image segmentation is particularly difficult due to restrictions imposed by image ...
Olabarriaga, S.D., Smeulders, A.W.M.
exaly   +6 more sources

DRINet for Medical Image Segmentation

IEEE Transactions on Medical Imaging, 2018
Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The U-Net architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution
Liang Chen 0018, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert   +5 more
openaire   +4 more sources