151. Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images
- Author
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Francesca Pugliese, Hannah Safi, Xingru Huang, Mick Ozkor, Ryo Torii, Retesh Bajaj, Jouke Dijkstra, Andreas Baumbach, Anantharaman Ramasamy, Patrick W. Serruys, Yakup Kilic, James C. Moon, Anthony Mathur, Christos V. Bourantas, Ajay Jain, Emrah Erdogan, Qianni Zhang, and Vincenzo Tufaro
- Subjects
Lumen (anatomy) ,High resolution ,030204 cardiovascular system & hematology ,Accurate segmentation ,Standard deviation ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,Intravascular ultrasound ,Machine learning ,Medicine ,Humans ,Segmentation ,030212 general & internal medicine ,Ultrasonography, Interventional ,Ultrasonography ,Image segmentation ,medicine.diagnostic_test ,business.industry ,Deep learning ,Pattern recognition ,Coronary Vessels ,Plaque, Atherosclerotic ,cardiovascular system ,Artificial intelligence ,Cardiology and Cardiovascular Medicine ,business - Abstract
Aims The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. Methods and results IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754–1.061) with similar results in frames portraying calcific plaques or side branches. Conclusions The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research.
- Published
- 2021