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A

Amal Lahchim

University of Patras, Greece

L

Lambros Athanasiou

University of Patras, Greece

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References (24)

[1]
Deep learning model DeepNeo predicts neointimal tissue characterization using optical coherence tomography
2025V. Koch, O. Holmberg et al.
[2]
Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review
2025Juhwan Lee, Y. Gharaibeh et al.
[3]
Learning With Fewer Images via Image Clustering: Application to Intravascular OCT Image Segmentation
2021Chaitanya Kolluru, Juhwan Lee et al.
[4]
Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features
2020Juhwan Lee, D. Prabhu et al.
[5]
Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function.
2020B. Qiu, Zhiyu Huang et al.
[6]
Fully Automated Lumen Segmentation Method for Intracoronary Optical Coherence Tomography
2018Elżbieta Pociask, K. Malinowski et al.
[7]
Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images
2018Chaitanya Kolluru, D. Prabhu et al.
[8]
ARC-OCT: Automatic detection of lumen border in intravascular OCT images
2017Grigorios-Aris Cheimariotis, Y. Chatzizisis et al.
[9]
Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set
2017Yihui Cao, K. Cheng et al.
[10]
Automatic classification of atherosclerotic plaques imaged with intravascular OCT.
2016Jose J. Rico-Jimenez, D. U. Campos-Delgado et al.
[11]
In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading
2014Simona Celi, S. Berti
[12]
Automatic lumen segmentation in IVOCT images using binary morphological reconstruction
2013M. C. Moraes, D. Cárdenas et al.
[13]
A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina
2013R. Kafieh, H. Rabbani et al.
[14]
Automatic segmentation of in-vivo intra-coronary optical coherence tomography images to assess stent strut apposition and coverage
2012G. Ughi, T. Adriaenssens et al.
[15]
Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography.
2011S. Tsantis, G. Kagadis et al.
[16]
Image Segmentation by Using Threshold Techniques
2010Salem Saleh Al-amri, N. Kalyankar et al.
[17]
Intracoronary optical coherence tomography: a comprehensive review clinical and research applications.
2009H. Bezerra, M. Costa et al.
[18]
A New 3-D Automated Computational Method to Evaluate In-Stent Neointimal Hyperplasia in In-Vivo Intravascular Optical Coherence Tomography Pullbacks
2009Serhan Gurmeric, Gözde Gül Şahin et al.
[19]
Assessment of culprit lesion morphology in acute myocardial infarction: ability of optical coherence tomography compared with intravascular ultrasound and coronary angioscopy.
2007T. Kubo, T. Imanishi et al.
[20]
Assessment of coronary arterial thrombus by optical coherence tomography.
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Showing 20 of 24 references

Founder's Pitch

"Automated intracoronary OCT processing for real-time vessel identification and classification to aid clinicians."

Medical ImagingScore: 6View PDF ↗

Commercial Viability Breakdown

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2.5

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4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

This research addresses a critical need in the medical field by providing an automated method to process and analyze intracoronary OCT images, reducing the dependency on manual interpretation which is time-consuming and prone to error.

Product Angle

Develop a software tool that integrates this image processing pipeline into existing OCT systems, enabling automated, real-time analysis and classification of intracoronary images.

Disruption

The solution has the potential to replace manual OCT image analysis in hospitals, which is currently labor-intensive and less reliable.

Product Opportunity

The cardiovascular imaging market is vast, with hospitals, clinics, and research institutions willing to pay for solutions that improve diagnostic accuracy and reduce operational time.

Use Case Idea

Real-time OCT data processing tool for hospitals and clinics to support cardiovascular diagnostics and interventions.

Science

The paper presents a process pipeline for intracoronary OCT image processing using k-means clustering and machine learning models like Logistic Regression and SVM for vessel classification. This method results in high accuracy and precision.

Method & Eval

The method involved transforming polar to Cartesian coordinates for better segmentation, followed by clustering and machine learning for classification, achieving 99.68% accuracy and precision of 1.00.

Caveats

The approach might face challenges in adapting to different OCT systems and may require extensive validation across diverse clinical scenarios. Also, lack of code sharing reduces transparency and adoption.

Author Intelligence

Amal Lahchim

University of Patras, Greece
1up1120133@upatras.gr

Lambros Athanasiou

University of Patras, Greece
2lmathanas@uoi.gr