Jan 28, 2024 · This method introduces the time information feature between slices in three-dimensional (3D) image data on the basis of a two-dimensional (2D) segmentation ...
Jan 29, 2024 · This method introduces the time information feature between slices in three-dimensional (3D) image data on the basis of a two-dimensional (2D) segmentation ...
A 2.5D Stroke Lesion Segmentation Method Based on Multi-slice ...
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Nov 21, 2024 · This method introduces the time information feature between slices in three-dimensional (3D) image data on the basis of a two-dimensional (2D) ...
A 2.5D stroke lesion segmentation method based on the fusion of multi-slice features is proposed in this study. This method introduces the time information ...
MSMV-UNet: A 2.5D Stroke Lesion Segmentation Method Based on Multi-slice Feature Fusion. MMM (3) 2024: 57-69. [c2]. view. electronic edition via DOI ...
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This paper proposes a new architecture called dimension-fusion-UNet (D-UNet), which combines 2D and 3D convolution innovatively in the encoding stage. The ...
MSMV-UNet: A 2.5D Stroke Lesion Segmentation Method Based on Multi-slice Feature Fusion. Jingjing Xie, JiXuan Hong, Manjin Sheng, Chenhui Yang. Pages 57-69 ...
Dec 9, 2024 · MSMV-UNet: A 2.5D Stroke Lesion Segmentation Method Based on Multi-slice Feature Fusion. Chapter. Jan 2024; Lect Notes Comput Sci. JingJing Xie ...
MSMV-UNet: A 2.5D Stroke Lesion Segmentation Method Based on Multi-slice Feature Fusion. Jingjing Xie, JiXuan Hong, Manjin Sheng, Chenhui Yang. https://doi ...
To meet this challenge, this paper proposes a cross-attention and deep supervision UNet (CADS-UNet) to segment chronic stroke lesions from T1-weighted MR images ...