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Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation

Title
Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
Authors
Ullah, IhsanChikontwe, PhilipChoi, HongsooYoon, Chang HwanPark, Sang Hyun
DGIST Authors
Ullah, Ihsan; Chikontwe, Philip; Choi, Hongsoo; Yoon, Chang Hwan; Park, Sang Hyun
Issue Date
2021-02
Citation
Applied Sciences, 11(4), 1638
Type
Article
Author Keywords
Adversarial learningCatheter robotConvolutional neural networksImage translation
ISSN
2076-3417
Abstract
Automatic catheter and guidewire segmentation plays an important role in robot-assisted interventions that are guided by fluoroscopy. Existing learning based methods addressing the task of segmentation or tracking are often limited by the scarcity of annotated samples and difficulty in data collection. In the case of deep learning based methods, the demand for large amounts of labeled data further impedes successful application. We propose a synthesize and segment approach with plug in possibilities for segmentation to address this. We show that an adversarially learned image-to-image translation network can synthesize catheters in X-ray fluoroscopy enabling data augmentation in order to alleviate a low data regime. To make realistic synthesized images, we train the translation network via a perceptual loss coupled with similarity constraints. Then existing segmentation networks are used to learn accurate localization of catheters in a semi-supervised setting with the generated images. The empirical results on collected medical datasets show the value of our approach with significant improvements over existing translation baseline methods. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/20.500.11750/13475
DOI
10.3390/app11041638
Publisher
MDPI AG
Related Researcher
  • Author Choi, Hongsoo Bio-Micro Robotics Lab
  • Research Interests Micro/Nano robot; Neural prostheses; MEMS; BMI; MEMS/NEMS; BioMEMS; MEMS 초음파 트랜스듀스; 인공와우
Files:
Collection:
Department of Robotics and Mechatronics EngineeringBio-Micro Robotics Lab1. Journal Articles
Department of Robotics and Mechatronics EngineeringMedical Image & Signal Processing Lab1. Journal Articles


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