Results 11 to 20 of about 914,658 (364)

UNet++: A Nested U-Net Architecture for Medical Image Segmentation [PDF]

DLMIA/ML-CDS@MICCAI, 2018
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of ...
Liang, Jianming, Siddiquee, Md Mahfuzur Rahman, Tajbakhsh, Nima, Zhou, Zongwei   +3 more
core   +2 more sources

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

Bioengineering
Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques,
Yan Xu, Rixiang Quan, Weiting Xu, Yi Huang, Xiaolong Chen, Fengyuan Liu   +5 more
doaj   +2 more sources

Deep learning for medical image segmentation: State-of-the-art advancements and challenges

Informatics in Medicine Unlocked
Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in
Md. Eshmam Rayed, S.M. Sajibul Islam, Sadia Islam Niha, Jamin Rahman Jim, Md Mohsin Kabir, M.F. Mridha   +5 more
doaj   +2 more sources

Advantages of transformer and its application for medical image segmentation: a survey

BioMedical Engineering OnLine
Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models.
Qiumei Pu, Zuoxin Xi, Shuai Yin, Zhe Zhao, Lina Zhao   +4 more
doaj   +2 more sources

UNETR: Transformers for 3D Medical Image Segmentation [PDF]

IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021
Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global
Ali Hatamizadeh, Dong Yang, H. Roth, Daguang Xu   +3 more
semanticscholar   +1 more source

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [PDF]

Medical Image Analysis, 2023
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface.
Junde Wu, Rao Fu, Huihui Fang, Yuanpei Liu, Zhao-Yang Wang, Yanwu Xu, Yueming Jin, T. Arbel   +7 more
semanticscholar   +1 more source

UNeXt: MLP-based Rapid Medical Image Segmentation Network [PDF]

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022
UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for rapid image segmentation in point-of-care applications as they are parameter-
Jeya Maria Jose Valanarasu, Vishal M. Patel   +1 more
semanticscholar   +1 more source

Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation [PDF]

Computer Vision and Pattern Recognition, 2023
In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and un-labeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data ...
Yunhao Bai, Duowen Chen, Qingli Li, Wei-lei Shen, Yan Wang   +4 more
semanticscholar   +1 more source

UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation [PDF]

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020
Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Huimin Huang, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yenwei Chen, Jian Wu   +8 more
semanticscholar   +1 more source

A Survey on Medical Image Segmentation Based on Deep Learning Techniques

Big Data and Cognitive Computing, 2022
Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent
Jayashree Moorthy, Usha Devi Gandhi
doaj   +1 more source