Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation (1806.07201v1)

Abstract: Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing deep network models require a large amount of labeled data. However, obtaining accurate pixel-wise labeling in X-ray images relies heavily on skilled clinicians due to the large overlaps of anatomy and the complex texture patterns. On the other hand, organs in 3D CT scans preserve clearer structures as well as sharper boundaries and thus can be easily delineated. In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans. Specifically, a Dense Image-to-Image network (DI2I) for multi-organ segmentation is first trained on X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT volumes. Then we introduce a Task Driven Generative Adversarial Network (TD-GAN) architecture to achieve simultaneous style transfer and parsing for unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure for pixel-to-pixel translation between DRRs and X-ray images and an added module leveraging the pre-trained DI2I to enforce segmentation consistency. The TD-GAN framework is general and can be easily adapted to other learning tasks. In the numerical experiments, we validate the proposed model on 815 DRRs and 153 topograms. While the vanilla DI2I without any adaptation fails completely on segmenting the topograms, the proposed model does not require any topogram labels and is able to provide a promising average dice of 85% which achieves the same level accuracy of supervised training (88%).

• The paper introduces a TD-GAN model that adapts DRR-based segmentation for unsupervised X-ray image analysis.
• It integrates a densely connected UNet with cycle-consistent GAN techniques to maintain segmentation fidelity during domain adaptation.
• Experimental findings reveal an 85% average Dice score, closely matching supervised benchmarks while reducing the need for manual annotations.

Insights from Task Driven Generative Modeling for Unsupervised Domain Adaptation in X-ray Image Segmentation

This paper explores the field of medical image analysis, specifically focusing on the challenging domain of unsupervised domain adaptation for X-ray image segmentation. The paper presents a framework termed Task Driven Generative Adversarial Network (TD-GAN) to address the intricacies involved in parsing multi-organ structures within X-ray images when annotated datasets are unavailable. Here, the authors propose a novel approach leveraging Digitally Reconstructed Radiographs (DRRs) sourced from labeled 3D CT scans to enable training without direct annotations of X-ray images.

Methodological Framework

The authors implement a multi-step approach starting with the creation of DRRs, which harbor clearer anatomical structures than traditional X-rays, thus enabling precise organ delineations. Initially, a Dense Image-to-Image Network (DI2I) is trained on these DRRs to establish a robust model for multi-organ segmentation. This DI2I utilizes a densely connected UNet architecture for efficient feature mapping across inputs, providing a foundation for accurate organ segmentation.

Following DRR-based training, the main innovation is introduced through TD-GAN, a model combining concepts from cycle-consistent GANs with segmentation-specific adaptations. This architecture incorporates a cycle-GAN structure with additional supervisory modules that ensure segmentation accuracy is maintained during style transfer from DRR to X-ray image domains.

Experimental Findings

The numerical experiments conducted with TD-GAN exemplify its efficiency. The model achieves a remarkable average Dice score of 85% on X-ray topogram segmentation, juxtaposed against a supervised benchmark of 88%, showcasing near-parity in efficacy without requiring any labeled X-ray data. The results emphasize the capability of TD-GAN to generalize learned representations from DRRs to real X-ray images, thereby reducing dependency on extensive manual annotations typically necessitated for training deep models.

Theoretical and Practical Implications

This research provides substantive evidence for the applicability of generative adversarial frameworks in unsupervised domain adaptation specific to medical imaging applications. The paper delineates how transferring knowledge from one domain to another (i.e., DRRs to X-rays) through style adaptation can be further optimized by task-specific supervision, a notable contribution for medical imaging tasks. From a theoretical standpoint, the approach highlights the potential for deploying GANs not merely for style transfer but also as a mechanism for task-oriented adaptations, offering possibilities for extending beyond segmentation.

Practically, TD-GAN implies that segmentation models could be trained with significantly reduced annotation burdens in medical applications, where acquiring labeled datasets is costly and time-consuming. This has meaningful implications for clinical workflows, potentially easing the development of computer-aided diagnostic systems capable of handling diverse imaging modalities.

Speculations for Future Developments

The generality of the proposed framework suggests a wider application spectrum, including other types of medical diagnostic tasks beyond segmentation. Future research might explore the integration of such models with different supervisory networks tailored to specific clinical requirements or modalities. Continued exploration into the stability and scalability of this approach will determine its broader utility in the field of medical image analysis and potentially in other image-centric domains where labeled data scarcity is a prevalent challenge.

In conclusion, this research makes a significant contribution by demonstrating an effective unsupervised learning strategy for medical image segmentation, setting a precedent for leveraging generative models in complex domain adaptation tasks.