Results 41 to 50 of about 6,997,548 (275)

COMPARISON OF 2D AND 3D-BASED U-NET ARCHITECTURES FOR IMAGE RESTORATION IN STRUCTURED ILLUMINATION MICROSCOPY [PDF]

, 2023
Recent developments using deep learning (DL) super-resolution in structured-illumination microscopy (SIM), have improved speed in two-dimensional (2D) image restoration and minimized the impact of noise.
Kebede, Bereket
core  

U-NetCTS: U-Net deep neural network for fully automatic segmentation of 3D CT DICOM volume

, 2022
The accurate segmentation of computed tomography (CT) scan volume is an essential step in radiomic analysis as well as in developing advanced surgical planning techniques with numerous medical applications.
O. Dorgham, Dorgham, O, Mirjalili, S, A. Hyari, Al-Najdawi, N, M.H. Ryalat, S. Mirjalili, Hyari, A, M. Abu Naser, Naser, MA, Ryalat, MH, N. Al-Najdawi   +11 more
core   +1 more source

A deep learning aided bone marrow segmentation of quantitative fat MRI for myelofibrosis patients

Frontiers in Oncology
PurposeTo automate bone marrow segmentation within pelvic bones in quantitative fat MRI of myelofibrosis (MF) patients using deep-learning (DL) U-Net models.MethodsAutomated segmentation of bone marrow (BM) was evaluated for four U-Net models: 2D U-Net ...
Humera Tariq, Lubomir Hadjiiski, Dariya Malyarenko, Moshe Talpaz, Kristen Pettit, Gary D. Luker, Brian D. Ross, Thomas L. Chenevert   +7 more
doaj   +1 more source

Implicit U-Net for Volumetric Medical Image Segmentation [PDF]

, 2022
U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images.
Giacomo Tarroni, Marimont, S. N., Sergio Naval Marimont, Tarroni, G.   +3 more
core   +2 more sources

Crossline Reconstruction of 3D Seismic Data Using 3D cWGAN: A Comparative Study on Sleipner Seismic Survey Data

Applied Sciences, 2023
In seismic data acquisition, data loss can occur, particularly with the use of streamer systems in marine seismic exploration. These systems often cause spatial aliasing problems by having close inline intervals and wide crossline intervals to maximize ...
Jiyun Yu, Daeung Yoon
doaj   +1 more source

3D U-NET FOR BRAIN TUMOUR SEGMENTATION

, 2023
The brain is the most complex part of the human body that controls memory, emotions, touch, motor, thought, skills, vision, breathing, temperature and every process related to regulating the human body. Usually, the brain tumour can be classified as malignant or benign, and it can spread to other regions as sometimes not.
openaire   +2 more sources

Analysis of AI-Based Single-View 3D Reconstruction Methods for an Industrial Application

Sensors, 2022
Machine learning (ML) is a key technology in smart manufacturing as it provides insights into complex processes without requiring deep domain expertise.
Julia Hartung, Patricia M. Dold, Andreas Jahn, Michael Heizmann   +3 more
doaj   +1 more source

LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net

, 2019
We propose LU-Net---for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet, we propose an end-to-end architecture for LiDAR point cloud semantic ...
AUJOL, Jean-François, Aurelie Bugeau, Mathieu Bredif, Pierre Biasutti, Jean-Francois Aujol, BIASUTTI, Pierre, LEPETIT, Vincent, BUGEAU, Aurélie, BRÉDIF, Mathieu, Vincent Lepetit   +9 more
core   +1 more source

Residual tail twisting in ascidian larvae is stabilized by asymmetric myofibrils that resist bilateral symmetry restoration

FEBS Letters, EarlyView.
Ascidian Ciona larvae initially show strong clockwise tail twisting, which is largely corrected during development. However, a small residual twist remains. This study shows that organized helical myofibrils in tail muscles mechanically stabilize this residual asymmetry, preventing complete restoration of bilateral symmetry and revealing how embryos ...
Yuki S. Kogure, Misaki K. Nishio, Satoru Okuda, Kohji Hotta   +3 more
wiley   +1 more source

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network

Scientific Reports, 2020
Segmentation of normal organs is a critical and time-consuming process in radiotherapy. Auto-segmentation of abdominal organs has been made possible by the advent of the convolutional neural network.
Hojin Kim, Jinhong Jung, Jieun Kim, Byungchul Cho, Jungwon Kwak, Jeong Yun Jang, Sang-wook Lee, June-Goo Lee, Sang Min Yoon   +8 more
doaj   +1 more source