Dataset Information

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 the Cancer Imaging Archive and unlabeled T2w MRIs from a completely unrelated cohort of 6 patients with pre-treatment and 36 on-treatment scans. Next, we combined 6 labeled pre-treatment MRI scans with the synthesized MRIs to boost tumor segmentation accuracy through semi-supervised learning. Semi-supervised training of cycle-GAN produced a segmentation accuracy of 0.66 computed using Dice Score Coefficient (DSC). Our method trained with only synthesized MRIs produced an accuracy of 0.74 while the same method trained in semi-supervised setting produced the best accuracy of 0.80 on test. Our results show that tumor-aware adversarial domain adaptation helps to achieve reasonably accurate cancer segmentation from limited MRI data by leveraging large CT datasets.

SUBMITTER: Jiang J

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

REPOSITORIES: biostudies-literature

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

Jiang Jue J Hu Yu-Chi YC Tyagi Neelam N Zhang Pengpeng P Rimner Andreas A Mageras Gig S GS Deasy Joseph O JO Veeraraghavan Harini H

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 20180901

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 ...[more]

PMID: 30294726

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Dataset Information

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

Publications

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

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