Results 21 to 30 of about 4,013,335 (305)

Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach

Sensors, 2021
The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS
Seok Oh, Young-Jae Kim, Young-Taek Park, Kwang-Gi Kim   +3 more
doaj   +1 more source

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

, 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

Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer

Frontiers in Oncology, 2021
Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms based
Juebin Jin, Haiyan Zhu, Haiyan Zhu, Jindi Zhang, Yao Ai, Ji Zhang, Yinyan Teng, Congying Xie, Congying Xie, Xiance Jin   +9 more
doaj   +1 more source

Table1_Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network.DOCX [PDF]

, 2022
Objectives: We developed a 3D U-Net-based deep convolutional neural network for the automatic segmentation of the vertebral cortex. The purpose of this study was to evaluate the accuracy of the 3D U-Net deep learning model.Methods: In this study, a fully
Shanshan Li (114847), Yang Li (7082), Hui Qiu (120319), Jian Qin (34092), Haitao Yu (456678), Xiaofeng Xie (551923), Changqin Li (8834486), Qianqian Yao (753723), Zeren Shi (13979757)   +8 more
core   +1 more source

TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation

CoRR, 2021
8 page, 3 ...
Yao Chang, Menghan Hu, Guangtao Zhai, Xiao-Ping Zhang 0002   +3 more
openaire   +2 more sources

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation

Journal of Medical Internet Research, 2022
BackgroundDetection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients.
Hoon Ko, Jimi Huh, Kyung Won Kim, Heewon Chung, Yousun Ko, Jai Keun Kim, Jei Hee Lee, Jinseok Lee   +7 more
doaj   +1 more source

Segmentation and recognition of breast ultrasound images based on an expanded U-Net.

PLoS ONE, 2021
This paper establishes a fully automatic real-time image segmentation and recognition system for breast ultrasound intervention robots. It adopts the basic architecture of a U-shaped convolutional network (U-Net), analyses the actual application ...
Yanjun Guo, Xingguang Duan, Chengyi Wang, Huiqin Guo   +3 more
doaj   +1 more source

Is the U-NET Directional-Relationship Aware?

2022 IEEE International Conference on Image Processing (ICIP), 2022
Accepted at ICIP ...
Riva, Mateus, Gori, Pietro, Yger, Florian, Bloch, Isabelle   +3 more
openaire   +3 more sources

U-Net for leaf vein segmentation. [PDF]

, 2023
Images of the high-quality dataset (A) were converted to grayscale images and masked vein images were generated by conventional image processing with contrast enhancement and binarization (B) to prepare a training dataset.
Kohei Iwamasa (16624443), Koji Noshita (3326502)   +1 more
core   +1 more source

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

IEEE Access, 2023
The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be time-consuming and delay diagnosis.
Kittipol Wisaeng
doaj   +1 more source