Structure Unbiased Adversarial Model for Medical Image Segmentation

TL;DR

This paper introduces the SUA network, a novel adversarial model that effectively aligns structural and intensity differences in medical images to improve segmentation accuracy across datasets.

Contribution

The paper proposes a new Structure-Unbiased Adversarial (SUA) network that incorporates spatial transformation and intensity rendering to address structural and intensity discrepancies in medical image segmentation.

Findings

SUA effectively transfers structural content between datasets.

SUA improves segmentation performance across multiple datasets.

The model reduces the structural gap in medical images.

Abstract

Generative models have been widely proposed in image recognition to generate more images where the distribution is similar to that of the real ones. It often introduces a discriminator network to differentiate the real data from the generated ones. Such models utilise a discriminator network tasked with differentiating style transferred data from data contained in the target dataset. However in doing so the network focuses on discrepancies in the intensity distribution and may overlook structural differences between the datasets. In this paper we formulate a new image-to-image translation problem to ensure that the structure of the generated images is similar to that in the target dataset. We propose a simple, yet powerful Structure-Unbiased Adversarial (SUA) network which accounts for both intensity and structural differences between the training and test sets when performing image segmentation. It consists of a spatial transformation block followed by an intensity distribution rendering module. The spatial transformation block is proposed to reduce the structure gap between the two images, and also produce an inverse deformation field to warp the final segmented image back. The intensity distribution rendering module then renders the deformed structure to an image with the target intensity distribution. Experimental results show that the proposed SUA method has the capability to transfer both intensity distribution and structural content between multiple datasets.