Results 191 to 200 of about 6,662,539 (239)

Bone image segmentation

IEEE Transactions on Biomedical Engineering, 1999
Characteristics of microscopic structures in bone cross sections carry essential clues in age determination in forensic science and in the study of age-related bone developments and bone diseases. Analysis of bone cross sections represents a major area of research in bone biology. However, traditional approaches in bone biology have relied primarily on
Z Q, Liu, H L, Liew, J G, Clement, C D, Thomas   +3 more
openaire   +2 more sources

TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021
Medical image segmentation - the prerequisite of numerous clinical needs - has been significantly prospered by recent advances in convolutional neural networks (CNNs). However, it exhibits general limitations on modeling explicit long-range relation, and
Yundong Zhang, Huiye Liu, Qiang Hu
semanticscholar   +1 more source

Image segmentation techniques

Computer Vision, Graphics, and Image Processing, 1984
There are now a wide Abstract There are now a wide variety of image segmentation techniques, some considered general purpose and some designed for specific classes of images. These techniques can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid linkage region growing schemes, centroid linkage ...
Robert M. Haralick, Linda G. Shapiro
openaire   +1 more source

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

European Conference on Computer Vision, 2018
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling
Liang-Chieh Chen, Yukun Zhu, G. Papandreou, Florian Schroff, Hartwig Adam   +4 more
semanticscholar   +1 more source

U-Net: Convolutional Networks for Biomedical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available ...
O. Ronneberger, P. Fischer, T. Brox
semanticscholar   +1 more source

Scaling Open-Vocabulary Image Segmentation with Image-Level Labels

European Conference on Computer Vision, 2021
We design an open-vocabulary image segmentation model to organize an image into meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite attaining impressive open-vocabulary classification accuracy with image-level caption ...
Golnaz Ghiasi, Xiuye Gu, Yin Cui, Tsung-Yi Lin   +3 more
semanticscholar   +1 more source

nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

IEEE Transactions on Image Processing, 2021
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks
Hong-Yu Zhou, J. Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu   +5 more
semanticscholar   +1 more source

UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021
Transformer architecture has emerged to be successful in a number of natural language processing tasks. However, its applications to medical vision remain largely unexplored.
Yunhe Gao, Mu Zhou, Dimitris N. Metaxas
semanticscholar   +1 more source

H2Former: An Efficient Hierarchical Hybrid Transformer for Medical Image Segmentation

IEEE Transactions on Medical Imaging, 2023
Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very important
Along He, Kai Wang, Tao Li, Chengkun Du, S. Xia, H. Fu   +5 more
semanticscholar   +1 more source

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

arXiv.org
Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational ...
Jun Ma, Feifei Li, Bo Wang
semanticscholar   +1 more source