Combining external-latent attention for medical image segmentation

Song, Enmin; Zhan, Bangcheng; Liu, Hong

doi:10.1016/j.neunet.2023.10.046

Cited by 9 publications

(1 citation statement)

References 27 publications

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“…Cai et al [23] utilized Convolutional Long Short-Term Memory (CLSTM) modules to address the problem of learning temporal features in pancreatic CT slice data. The CLSTM module achieves spatially consistent segmentation of individual slices for label predictions and further optimizes Convolutional Neural Network (CNN)-based segmentation results by considering the context features of the pancreas [24]. Heinrich et al [25] proposed a high-performance ternary network architecture aiming to reduce reliance on computer memory [22].…”

Section: Introductionmentioning

confidence: 99%

ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels

Ji,

Liu,

et al. 2024

Med Biol Eng Comput

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The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully utilizes the low-level and deep-level features extracted by the ResDAC blocks. Finally, parallel dilated convolutions are employed to increase the receptive field to capture multiscale spatial information. ResDAC-Net is highly compatible to the existing state-of-the-art models, according to three (out of four) evaluation metrics, including the two main ones used for segmentation performance evaluation (i.e., DSC and Jaccard index). Graphical abstract

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