Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation (2003.05056v1)

Abstract: 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, $PH^2$, and cell nuclei segmentation, achieving state-of-the-art performance.

• The paper introduces an enhanced U-Net architecture that integrates SE modules, dense convolutions, and BConvLSTM to improve feature recalibration and segmentation accuracy.
• The proposed method achieves superior performance on multiple datasets, with notable improvements such as an F1 score of 0.8224 and a Jaccard index of 0.9570.
• The study demonstrates a robust, resource-efficient framework for medical image segmentation, paving the way for advancements in computer-assisted diagnostics.

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

The paper "Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation" introduces an advanced architecture designed to enhance the performance of medical image segmentation tasks. Building upon the foundational U-Net model, the authors propose several augmentations, utilizing notable components such as Squeeze and Excitation (SE) modules, bi-directional ConvLSTM (BConvLSTM), and densely connected convolutions.

Key Contributions

1. Integration of SE Modules:

The authors incorporate SE modules within the U-Net to enhance feature recalibration and improve segmentation accuracy. These modules allow the network to adaptively adjust the contribution of each channel in the feature maps, employing a context gating mechanism to encode channel interdependencies without significantly increasing model complexity.

2. Dense Convolutional Layers:

The model employs densely connected convolutional layers at the end of the encoding path. This configuration facilitates enhanced feature propagation and reuse, leveraging the collective knowledge across the layers to learn a more diverse set of features. Such a design choice is motivated by efforts to prevent redundant feature extraction and improve the model's performance.

3. BConvLSTM for Improved Feature Fusion:

In place of the standard concatenation used in U-Net's skip connections, BConvLSTM is employed. This approach merges features from the encoder pathway and the decoder’s preceding layer in a non-linear manner. This integration enhances the network's ability to capture both spatial and temporal correlations in the feature space.

Evaluation and Results

The proposed method was evaluated on six datasets: DRIVE, ISIC 2017, ISIC 2018, lung segmentation, $PH^2$, and cell nuclei segmentation. The experiments demonstrated state-of-the-art performance across these datasets, highlighting the method’s efficacy in diverse medical image segmentation contexts.

• DRIVE Dataset: Showed superior performance with F1 score improvements reaching 0.8224.
• ISIC Datasets (2017 & 2018): Achieved impressive results with Jaccard Similarity indices of 0.9570 and 0.955 respectively.
• Lung Segmentation: The advancements yielded an accuracy of 0.9972.
• Performance Across Other Datasets: Consistently outperformed existing methods, indicating the robustness of the proposed approach.

Implications and Future Directions

The enhancements introduced by incorporating SE modules, dense connectivity, and BConvLSTM in the U-Net architecture present significant potential for developing more advanced AI systems capable of handling complex medical image analysis tasks. The approach's ability to achieve precise segmentations with relatively low model complexity suggests its utility in resource-limited applications.

For future research, exploring the integration of novel attention mechanisms and further reducing computational costs could be beneficial. Additionally, the framework could be applied to other domains requiring precise image segmentation, extending its impact beyond medical imaging.

Conclusion

In summary, "Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation" provides a robust framework for enhanced medical image segmentation. Through strategic architecture modifications, this approach achieves substantial performance gains and establishes a solid foundation for future innovations in computer-assisted medical diagnostics.