Results 21 to 30 of about 6,997,548 (275)
An attempt at beating the 3D U-Net [PDF]
Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19, 2019 The U-Net is arguably the most successful segmentation architecture in the medical domain. Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor Segmentation Challenge and attempt to improve upon it by augmenting it with residual and pre-activation residual blocks.
Isensee, Fabian, Maier-Hein, Klaus H.
openaire +3 more sources
Attention 3D-CU-Net: Enhancing Kidney Tumor Segmentation Accuracy Through Selective Feature Emphasis
IEEE Access, 2023 Kidney tumor segmentation from MR images became a pivotal research area in kidney cancer diagnosis and treatment planning. Accurate and efficient segmentation enables precise tumor localization, treatment planning, and monitoring of disease progression ...
S L Parvathi Sitanaboina +3 more
doaj +1 more source
, 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) +8 more
core +1 more source
3D U-Net++ model structure for tumor segmentation.
, 2023 3D U-Net++ model structure for tumor segmentation.
Kashfia Sailunaz (14295220) +5 more
core +1 more source
Attention Guided 3D U-Net for KiTS19 [PDF]
Submissions to the 2019 Kidney Tumor Segmentation Challenge: KiTS19, 2019 We use a two-stage 3d U-Net model to predict the multi channels segmentations from coarse to fine. The second stage is guided by the predictions from the first stage. 1 Method We proposed a two stages method to segment CT image from coarse to fine.
Zhong, Zhusi +2 more
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Random 2.5D U-net for Fully 3D Segmentation [PDF]
, 2019 Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and therefore, end-to-end training is limited by GPU memory and data size.
Christoph Angermann, Markus Haltmeier
openaire +4 more sources
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation [PDF]
, 2021 3D Lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume.
Nguyen, Tam +4 more
core +1 more source
A 3D attention U-Net network and its application in geological model parameterization
Petroleum Exploration and Development, 2023 To solve the problems of convolutional neural network–principal component analysis (CNN-PCA) in fine description and generalization of complex reservoir geological features, a 3D attention U-Net network was proposed not using a trained C3D video motion ...
Xiaobo LI +6 more
doaj +1 more source
, 2022 Download RDF Package3D U-Net trained using ZeroCostDL4Mic notebooks to segment mitochondria in Transmission Electron Microscopy (TEM) data.
DeepImageJ team
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3D U-Net Rodent Brain Mask Results
, 2021 This dataset is the results of testing data for '3D U-Net improves automatic brain extraction for isotropic rat brain MRI data'THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE.
Hsu, L (via Mendeley Data), Hsu, Li-Ming
core +1 more source