Tubular shape aware data generation for segmentation in medical imaging.

Autor: Sirazitdinov I; Philips Research, 42 Bol'shoy blvd, Moscow, Russia, 121205.; Skoltech, Bol'shoy blvd 30/1, Moscow, Russia, 121205., Schulz H; Philips Research, Philips GmbH Innovative Technologies, Röntgenstraße 24, 22335, Hamburg, Germany., Saalbach A; Philips Research, Philips GmbH Innovative Technologies, Röntgenstraße 24, 22335, Hamburg, Germany., Renisch S; Philips Research, Philips GmbH Innovative Technologies, Röntgenstraße 24, 22335, Hamburg, Germany., Dylov DV; Skoltech, Bol'shoy blvd 30/1, Moscow, Russia, 121205. d.dylov@skoltech.ru.
Jazyk: angličtina
Zdroj: 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.
DOI: 10.1007/s11548-022-02621-3
Abstrakt: 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.
(© 2022. CARS.)
Databáze: MEDLINE
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