PAN: Projective Adversarial Network for Medical Image Segmentation
This addresses the problem of efficient 3D medical image segmentation for clinicians, though it is incremental as it builds on existing adversarial learning methods.
The paper tackled the computational burden of capturing 3D semantics in medical image segmentation by proposing PAN, a projective adversarial network that uses 2D projections, achieving state-of-the-art performance for pancreas segmentation from CT scans without increasing segmentor complexity.
Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections. Furthermore, we introduce an attention module into our framework that helps for a selective integration of global information directly from our segmentor to our adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor.