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Medical Image Segmentation Based on 3D U-net
2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 2020For medical image processing, as the target area of the tumor lesions is small, and the boundaries of the organs are blurred, so the segmentation of the medical images is difficult. In the original 3D U-net model, feature extraction is performed on the interest image region by increasing the channel attention mechanism, so that the model keep a ...
Silu Chen, Guanghao Hu, Jun Sun
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3D Dense-U-Net for MRI Brain Tissue Segmentation
2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018This paper presents a fully automatic method for 3D segmentation of brain tissue on MRI scans using modern deep learning approach and proposes 3D Dense-U-Net neural network architecture using densely connected layers. In contrast with many previous methods, our approach is capable of precise segmentation without any preprocessing of the input image and
Martin Kolarík +3 more
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Liver Vessels Segmentation Based on 3d Residual U-NET
2019 IEEE International Conference on Image Processing (ICIP), 2019Recently, extraction of blood vessels has aroused widespread interests in medical image analysis. In this work, to accelerate convergence speed and enhance the representation for discriminative features, we introduce the residual block structure in the ResNet into the 3D U-Net, and construct a new 3D Residual U-Net architect to segment the hepatic and ...
Wei Yu +5 more
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3D U-Net for Brain Tumor Detection and Segmentation
2022We describe a fully automated brain tumor segmentation approach based on Convolutional Neural Network in this paper. The suggested network takes the 3D Flair Magnetic Resonance Image (MRI) of glioblastomas as input. These tumors can appear anywhere in the brain and have practically any shape or size by their very nature.
Swathi, P +4 more
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A preprocessing method based on 3D U-Net for abdomen segmentation
Computers in Biology and MedicineDeep learning methods have made significant progress in the field of biomedical automatic segmentation but still open to developments, especially due to the insufficient use of preprocessing methods. In this study, a pre-processing step is proposed both to improve segmentation performance and to produce faster segmentation results.
Hasan Basri Öksüz, Rahime Ceylan
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3D U-Net for Video Anomaly Detection
Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, 2021Jingxian Yang +3 more
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A 3D U-Net-Based Approach for Intracranial Aneurysm Detection
Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition, 2022Tianyu Zhu +6 more
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Residual 3D U-Net with Localization for Brain Tumor Segmentation
2022Marc Demoustier +4 more
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3D Patchwise U-Net with Transition Layers for MR Brain Segmentation
2019We propose a new patch based 3D convolutional neural network to automatically segment multiple brain structures on Magnetic Resonance (MR) images. The proposed network consists of encoding layers to extract informative features and decoding layers to reconstruct the segmentation labels.
Miguel Luna, Sang Hyun Park 0004
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