Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks

TL;DR

This paper introduces a novel spatially adaptive feature recombination and recalibration method, called SegSE, for Fully Convolutional Networks in semantic segmentation, improving accuracy across multiple medical imaging tasks.

Contribution

It proposes the SegSE block that adaptively recalibrates features considering spatial relevance, tailored for multi-object segmentation tasks.

Findings

Recombination and recalibration improve baseline segmentation performance.

Method generalizes well across brain tumor, stroke penumbra, and stroke outcome tasks.

Achieves state-of-the-art or competitive results in all tested applications.

Abstract

Fully Convolutional Networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts information from them. We observe that linear recombination of feature maps by increasing the number of channels followed by compression may enhance their discriminative power. Moreover, not all feature maps have the same relevance for the classes being predicted. In order to learn the inter-channel relationships and recalibrate the channels to suppress the less relevant ones, Squeeze and Excitation blocks were proposed in the context of image classification with Convolutional Neural Networks. However, this is not well adapted for segmentation with Fully Convolutional Networks since they segment several objects simultaneously, hence a feature map may contain relevant information only in some locations. In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks - the SegSE block. Feature maps are recalibrated by considering the cross-channel information together with spatial relevance. Experimental results indicate that Recombination and Recalibration improve the results of a competitive baseline, and generalize across three different problems: brain tumor segmentation, stroke penumbra estimation, and ischemic stroke lesion outcome prediction. The obtained results are competitive or outperform the state of the art in the three applications.