Purpose: Chest X-ray is one of the most widespread examinations of the human body. In interventional radiology, its use is frequently associated with the need to visualize various tube-like objects, such as puncture needles, guiding sheaths, wires, and catheters. Detection and precise localization of these tube-like objects in the X-ray images are, therefore, of utmost value, catalyzing the development of accurate target-specific segmentation algorithms. Similar to the other medical imaging tasks, the manual pixel-wise annotation of the tubes is a resource-consuming process. Methods: In this work, we aim to alleviate the lack of annotated images by using artificial data. Specifically, we present an approach for synthetic generation of the tube-shaped objects, with a generative adversarial network being regularized with a prior-shape constraint. Namely, our model uses Frangi-based regularization to draw synthetic tubes in the predefined fake mask regions and, then, uses the adversarial component to preserve the global realistic appearance of the synthesized image. Results: Our method eliminates the need for the paired image–mask data and requires only a weakly labeled dataset, with fine-tuning on a small paired sample (10–20 images) proving sufficient to reach the accuracy of the fully supervised models. Conclusion: We report the applicability of the approach for the task of segmenting tubes and catheters in the X-ray images, whereas the results should also hold for the other acquisition modalities and image computing applications that contain tubular objects.
|Journal||International Journal of Computer Assisted Radiology and Surgery|
|Publication status||Published - 17 Apr 2022|
- Generative adversarial network
- Neural network
- Shape analysis
- Weakly supervised segmentation
- X-ray imaging