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Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging
- Source :
- IEEE transactions on medical imaging. 39(5)
- Publication Year :
- 2019
-
Abstract
- Intracoronary imaging is a crucial imaging technology in coronary disease diagnosis as it visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in intracoronary images (VBDI) is desired because it can help the succeeding procedures of computer-aided disease diagnosis. However, existing VDBI methods suffer from the challenge of vessel-environment variability (i.e. high intra- and inter-subject diversity of vessels and their surrounding tissues appeared in images). This challenge leads to the ineffectiveness in the vessel region representation for hand-crafted features, in the receptive field extraction for deeply-represented features, as well as performance suppression derived from clinical data limitation. To solve this challenge, we propose a novel privileged modality distillation (PMD) framework for VBDI. PMD transforms the single-input-single-task (SIST) learning problem in the single-mode VBDI to a multiple-input-multiple-task (MIMT) problem by using the privileged image modality to help the learning model in the target modality. This learns the enriched high-level knowledge with similar semantics and generalizes PMD on diversity-increased low-level image features for improving the model adaptation to diverse vessel environments. Moreover, PMD refines MIMT to SIST by distilling the learned knowledge from multiple to one modality. This eliminates the reliance on privileged modality in the test phase, and thus enables the applicability to each of different intracoronary modalities. A structure-deformable neural network is proposed as an elaborately-designed implementation of PMD. It expands a conventional SIST network structure to the MIMT structure, and then recovers it to the final SIST structure. The PMD is validated on intravascular ultrasound imaging and optical coherence tomography imaging. One modality is the target, and the other one can be considered as the privileged modality owing to their semantic relatedness. The experiments show that our PMD is effective in VBDI (e.g. the Dice index is larger than 0.95), as well as superior to six state-of-the-art VBDI methods.
- Subjects :
- Computer science
Coronary Artery Disease
Coronary disease
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Optical coherence tomography
Intravascular ultrasound
medicine
Humans
Computer vision
Electrical and Electronic Engineering
Adaptation (computer science)
Modality (human–computer interaction)
Radiological and Ultrasound Technology
medicine.diagnostic_test
business.industry
Computer Science Applications
Coronary arteries
medicine.anatomical_structure
Imaging technology
Artificial intelligence
Neural Networks, Computer
business
Software
Tomography, Optical Coherence
Subjects
Details
- ISSN :
- 1558254X
- Volume :
- 39
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging
- Accession number :
- edsair.doi.dedup.....a251317b10f85ef999894cdc0973f09e