Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation.

ABSTRACT: We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images. In comparison, state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors. All networks were trained using labeled CT images from 377 patients with non-small cell lung cancer obtained from

SUBMITTER: Jiang J 

PROVIDER: S-EPMC6169798 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature