Modality-Specific Segmentation Network for Lung Tumor Segmentation in PET-CT Images

Lung tumor segmentation in PET-CT images plays an important role to assist physicians in clinical application to accurately diagnose and treat lung cancer. However, it is still a challenging task in medical image processing field. Due to respiration and movement, the lung tumor varies largely in PET images and CT images. Even the two images are almost simultaneously collected and registered, the shape and size of lung tumors in PET-CT images are different from each other. To address these issues, a modality-specific segmentation network (MoSNet) is proposed for lung tumor segmentation in PET-CT images. MoSNet can simultaneously segment the modality-specific lung tumor in PET images and CT images. MoSNet learns a modality-specific representation to describe the inconsistency between PET images and CT images and a modality-fused representation to encode the common feature of lung tumor in PET images and CT images. An adversarial method is proposed to minimize an approximate modality discrepancy through an adversarial objective with respect to a modality discriminator and reserve modality-common representation. This improves the representation power of the network for modality-specific lung tumor segmentation in PET images and CT images. The novelty of MoSNet is its ability to produce a modality-specific map that explicitly quantifies the modality-specific weights for the features in each modality. To demonstrate the superiority of our method, MoSNet is validated in 126 PET-CT images with NSCLC. Experimental results show that MoSNet outperforms state-of-the-art lung tumor segmentation methods.