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

By Jue Jiang, Yu-chi Hu, Neelam Tyagi, Pengpeng Zhang, A. Rimner, G. Mageras, J. Deasy, Harini Veeraraghavan. 2018

200 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pulmonary and Respiratory Medicine

1. Qi-Yan Dou
2. P. Heng
3. Lequan Yu
4. Hao Chen

In Radiology, Nuclear Medicine and Imaging

1. P. Heng
2. Qi-Yan Dou
3. Hao Chen
4. Lequan Yu
5. Jing Qin
6. Cheng Chen
7. Dong Yang
8. Stuart Crozier

Citations in the corpus

• Generative Adversarial Network in Medical Imaging: A Review (2018, neutral): “(2019) CycleGAN L1, 2, 3 – M11, 12 [ ✗ ] Brain Jiang et al. (2018) CycleGAN ∗ L1, 2, 3, 7, 8 D8 M32 [ ✗ ] Lung”
• Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation (2019, methodology): “[41] presented a tumor-aware, adversarial domain adaptation method for MR image segmentation with unpaired CT and MR images by preserving tumors on synthesized MR images produced from CT image.”
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation (2020, background): “Generative adversarial network (GAN) based approaches are also popular recently for medical imaging [12,28,43].”
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation (2020, methodology): “situation due to the significant domain shift caused by the different physical principles of modalities [7], [36], [37], [42].”
• Manipulating Medical Image Translation with Manifold Disentanglement (2020, background): “For example, segmentation map to medical image [20], [21], MR reconstruction [22], [23], image denoising [24], [25] and cross-modality translation [26], [27], [28].”
• Self-derived organ attention for unpaired CT-MRI deep domain adaptation based MRI segmentation (2020, methodology): “Hence, methods that only match the global image statistics lead to less accurate image-to-image (I2I) translations, and often result in loss of structures like tumors (Cohen et al 2018, Jiang et al 2018a).”
• An Asymetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans (2020, background): “Over the last three years, numerous similar approaches were effectively applied in the field of medical image analysis [3,4,5].”
• Domain Adaptation for Medical Image Analysis: A Survey (2021, unknown): “Jiang et al. [121], [122] propose to employ CycleGAN to generate MRIs from CT scans for lung tumor segmentation.”