Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network

TL;DR

This paper introduces CPGAN, a semi-supervised generative adversarial network that effectively segments brain stroke lesions with fewer labeled samples by leveraging a novel perception strategy and feature aggregation.

Contribution

The paper proposes a novel CPGAN model with a similarity connection module and consistent perception strategy for semi-supervised stroke lesion segmentation, reducing reliance on fully labeled data.

Findings

Outperforms existing methods with fewer labeled samples

Achieves superior segmentation accuracy on ATLAS dataset

Uses only two-fifths of labeled data to outperform fully supervised approaches

Abstract

The state-of-the-art deep learning methods have demonstrated impressive performance in segmentation tasks. However, the success of these methods depends on a large amount of manually labeled masks, which are expensive and time-consuming to be collected. In this work, a novel Consistent PerceptionGenerative Adversarial Network (CPGAN) is proposed for semi-supervised stroke lesion segmentation. The proposed CPGAN can reduce the reliance on fully labeled samples. Specifically, A similarity connection module (SCM) is designed to capture the information of multi-scale features. The proposed SCM can selectively aggregate the features at each position by a weighted sum. Moreover, a consistent perception strategy is introduced into the proposed model to enhance the effect of brain stroke lesion prediction for the unlabeled data. Furthermore, an assistant network is constructed to encourage the discriminator to learn meaningful feature representations which are often forgotten during training stage. The assistant network and the discriminator are employed to jointly decide whether the segmentation results are real or fake. The CPGAN was evaluated on the Anatomical Tracings of Lesions After Stroke (ATLAS). The experimental results demonstrate that the proposed network achieves superior segmentation performance. In semi-supervised segmentation task, the proposed CPGAN using only two-fifths of labeled samples outperforms some approaches using full labeled samples.