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Tubular shape aware data generation for segmentation in medical imaging.
- Source :
-
International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2022 Jun; Vol. 17 (6), pp. 1091-1099. Date of Electronic Publication: 2022 Apr 17. - Publication Year :
- 2022
-
Abstract
- 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.<br />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.<br />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.<br />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.<br /> (© 2022. CARS.)
- Subjects :
- Humans
Radiography
Algorithms
Image Processing, Computer-Assisted methods
Subjects
Details
- Language :
- English
- ISSN :
- 1861-6429
- Volume :
- 17
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- International journal of computer assisted radiology and surgery
- Publication Type :
- Academic Journal
- Accession number :
- 35430716
- Full Text :
- https://doi.org/10.1007/s11548-022-02621-3