Results 41 to 50 of about 340,403 (177)

Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound

, 2018
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is desirable, since manual delineations are time-consuming, challenging and ...
A Rohner, AT Hoven van den, C Baur, C Knackstedt, K Kamnitsas, N Almeida, O Oktay, RM Lang, RR Buechel   +8 more
core   +1 more source

Efficient Subclass Segmentation in Medical Images

, 2023
As research interests in medical image analysis become increasingly fine-grained, the cost for extensive annotation also rises. One feasible way to reduce the cost is to annotate with coarse-grained superclass labels while using limited fine-grained annotations as a complement.
Dai, Linrui, Lei, Wenhui, Zhang, Xiaofan
openaire   +2 more sources

Distributed contrastive learning for medical image segmentation

Medical Image Analysis, 2022
arXiv admin note: substantial text overlap with arXiv:2204 ...
Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu   +4 more
openaire   +3 more sources

Keypoint Transfer for Fast Whole-Body Segmentation

, 2018
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified distinctive image locations, where each keypoint correspondence suggests a ...
Golland, Polina, Langs, Georg, Toews, Matthew, Wachinger, Christian, Wells, William   +4 more
core   +1 more source

A review of medical ocular image segmentation

Virtual Reality & Intelligent Hardware
Deep learning has been extensively applied to medical image segmentation, resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U-Net in 2015.
Lai WEI, Menghan HU
doaj   +1 more source

Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

, 2017
We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention
Cobzas, Dana, Jagersand, Martin, Jaremko, Jacob L., Tang, Min, Zhang, Zichen   +4 more
core   +1 more source

Robust T-Loss for Medical Image Segmentation

Medical Image Analysis, 2023
This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sensitivity with a single parameter.
Alvaro Gonzalez-Jimenez, Simone Lionetti, Philippe Gottfrois, Fabian Gröger, Alexander Navarini, Marc Pouly   +5 more
openaire   +2 more sources

Self-Paced Learning With Diversity for Medical Image Segmentation by Using the Query-by-Committee and Dynamic Clustering Techniques

IEEE Access, 2021
Convolutional neural networks (CNNs), as a typical deep learning technique, have been widely used in image segmentation, but they often require a large amount of annotated data.
Xiaoying Pan, De Wei, Yizhe Zhao, Minjie Ma, Hao Wang   +4 more
doaj   +1 more source

Automatically Designing CNN Architectures for Medical Image Segmentation

, 2018
Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process. This process requires huge amount of time, expertise, and resources.
A Mortazi, KO Stanley, O Ronneberger
core   +1 more source

Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT

Journal of Applied Mathematics, 2014
Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places.
Seung-Hoon Chae, Daesung Moon, Deok Gyu Lee, Sung Bum Pan   +3 more
doaj   +1 more source