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E-Res U-Net: An improved U-Net model for segmentation of muscle images
Expert Systems with Applications, 2021Abstract In this paper, we propose a new semantic segmentation network called ’E-Res U-Net’, to achieve better segmentation results of deep and superficial muscles in ultrasonic muscle images. This model is based on U-Net, and its structure has been modified to improve the performance of the algorithm.
Junsheng Zhou +4 more
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Improved segmentation by adversarial u-net
Medical Imaging 2021: Computer-Aided Diagnosis, 2021Medical image segmentation has a fundamental role in many computer-aided diagnosis (CAD) applications. Accurate segmentation of medical images is a key step in tracking changes over time, contouring during radiotherapy planning, and more. One of the state-of-the-art models for medical image segmentation is the U–Net that consists of an encoder-decoder ...
David Sriker +3 more
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MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation
2021It is a challenging task to segment brain tumors from multi-modality MRI scans. How to segment and reconstruct brain tumors more accurately and faster remains an open question. The key is to effectively model spatial-temporal information that resides in the input volumetric data.
Changchen Zhao +3 more
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Information Flow Through U-Nets
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021Deep Neural Networks (DNNs) have become ubiquitous in medical image processing and analysis. Among them, U-Nets are very popular in various image segmentation tasks. Yet, little is known about how information flows through these networks and whether they are indeed properly designed for the tasks they are being proposed for.
Suemin Lee, Ivan V. Bajic
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A variational U‐Net for motion retargeting
Computer Animation and Virtual Worlds, 2020AbstractMotion retargeting is the process of copying motion from one character (source) to another (target) when the source and target body sizes and proportions (of arms, legs, torso, etc.) are different. The problem of automatic motion retargeting has been studied for several decades; however, the motion quality obtained with the application of ...
Seong Uk Kim +2 more
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A variational U-Net for motion retargeting
SIGGRAPH Asia 2018 Posters, 2018In this paper, we present a novel motion retargeting system by using the deep autoencoder combining the Deep Convolution Inverse Graphics Network (DC-IGN) ([Kulkarni et al. 2015]) and the U-Net ([Long et al. 2015]) to produce high-quality human motion. The retargeted motion is fully-automatically and naturally generated from the given input motion and ...
Hanyoung Jang +4 more
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2020
Lung cancer (lung carcinoma) is a malignant tumor defined by unrestrained cell growth in lung tissues. Long-term tobacco smoking is the major cause of lung cancer. Radiographs and Computed Tomography (CT) are used to see the lung cancer. The diagnosis is performed by the process called bronchoscopy and can be confirmed by biopsy. CT is a lung screening
Sathishkumar, R. +2 more
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Lung cancer (lung carcinoma) is a malignant tumor defined by unrestrained cell growth in lung tissues. Long-term tobacco smoking is the major cause of lung cancer. Radiographs and Computed Tomography (CT) are used to see the lung cancer. The diagnosis is performed by the process called bronchoscopy and can be confirmed by biopsy. CT is a lung screening
Sathishkumar, R. +2 more
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On Improving 3D U-net Architecture
Proceedings of the 14th International Conference on Software Technologies, 2019This paper presents a review of various techniques for improving the performance of neural networks on segmentation task using 3D convolutions and voxel grids – we provide comparison of network with and without max pooling, weighting, masking out the segmentation results, and oversampling results for imbalanced training dataset. We also present changes
Roman Janovský +2 more
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