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

TL;DR

This paper introduces an enhanced U-Net architecture for medical image segmentation that incorporates Squeeze and Excitation blocks, bi-directional ConvLSTM, and dense convolutions, leading to improved accuracy across multiple datasets.

Contribution

The paper presents a novel U-Net extension that integrates SE modules, BConvLSTM, and dense convolutions to boost segmentation performance with minimal added complexity.

Findings

Achieved state-of-the-art results on six diverse datasets.

Enhanced feature propagation and reuse through dense convolutions.

Improved segmentation accuracy with SE modules and BConvLSTM.

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.