CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation

被引:2
作者
Zhang, Zhuo [1 ]
Wen, Yihan [2 ]
Zhang, Xiaochen [3 ]
Ma, Quanfeng [3 ]
机构
[1] Tiangong Univ, Sch Elect & Informat Engn, Tianjin 300387, Peoples R China
[2] Dalian Univ Technol, Int Sch Informat Sci & Engn, Dalian 116620, Peoples R China
[3] Tianjin Huanhu Hosp, Tianjin Cerebral Vasc & Neural Degenerat Dis Key L, Tianjin 300350, Peoples R China
关键词
Segmentation; ConvNeXt; Cross-dimension attention; MODEL; TRANSFORMER; ALGORITHM; CNN;
D O I
10.1007/s13534-023-00341-4
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Deep learning-based methods have recently shown great promise in medical image segmentation task. However, CNN-based frameworks struggle with inadequate long-range spatial dependency capture, whereas Transformers suffer from computational inefficiency and necessitate substantial volumes of labeled data for effective training. To tackle these issues, this paper introduces CI-UNet, a novel architecture that utilizes ConvNeXt as its encoder, amalgamating the computational efficiency and feature extraction capabilities. Moreover, an advanced attention mechanism is proposed to captures intricate cross-dimensional interactions and global context. Extensive experiments on two segmentation datasets, namely BCSD, and CT2USforKidneySeg, confirm the excellent performance of the proposed CI-UNet as compared to other segmentation methods.
引用
收藏
页码:341 / 353
页数:13
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