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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 ...
Zongwei Zhou, M. R. Siddiquee, Nima Tajbakhsh, Jianming Liang   +3 more
semanticscholar   +3 more sources

Road Extraction by Deep Residual U-Net [PDF]

IEEE Geoscience and Remote Sensing Letters, 2017
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area
Zhengxin Zhang, Qingjie Liu, Yunhong Wang   +2 more
semanticscholar   +3 more sources

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

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Scientific Reports, 2022
Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies.
Kang Hsu, Da-Yo Yuh, Shao-Chieh Lin, Pin-Sian Lyu, Guan-Xin Pan, Yi-Chun Zhuang, Chia-Ching Chang, Hsu-Hsia Peng, Tung-Yang Lee, Cheng-Hsuan Juan, Cheng-En Juan, Yi-Jui Liu, Chun-Jung Juan   +12 more
doaj   +1 more source

MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation [PDF]

Neural Networks, 2019
In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community.
Nabil Ibtehaz, Mohammad Sohel Rahman
semanticscholar   +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

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

A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation [PDF]

IEEE International Symposium on Biomedical Imaging, 2018
We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and ...
Nabila Abraham, N. Khan
semanticscholar   +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