Papers › Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation

Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation

10 Mar 2020arXiv:2003.05056archive 2025-07-28

Maryam Asadi-Aghbolaghi, Reza Azad, Mahmood Fathy, Sergio Escalera

Medical image segmentation has been very challenging due to the large variation of anatomy across different cases. Recent advances in deep learning frameworks have exhibited faster and more accurate performance in image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net for medical image segmentation, in which we take full advantages of U-Net, Squeeze and Excitation (SE) block, bi-directional ConvLSTM (BConvLSTM), and the mechanism of dense convolutions. (I) We improve the segmentation performance by utilizing SE modules within the U-Net, with a minor effect on model complexity. These blocks adaptively recalibrate the channel-wise feature responses by utilizing a self-gating mechanism of the global information embedding of the feature maps. (II) To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. (III) Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM in all levels of the network to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. The proposed model is evaluated on six datasets DRIVE, ISIC 2017 and 2018, lung segmentation, PH², and cell nuclei segmentation, achieving state-of-the-art performance.

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Code

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Tasks

Anatomy Image Segmentation Lesion Segmentation Medical Image Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Lesion Segmentation	ISIC 2018	MCGU-Net	mean Dice	0.895	#10 of 17	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Concatenated Skip Connection ConvLSTM Convolution Max Pooling ReLU Sigmoid Activation Tanh Activation U-Net

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