Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients
Intravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient’s condition, are better assessed by OCT images in comparison to other in vivo modalities. A l...
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MDPI AG
2021-08-01
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| author | Grigorios-Aris Cheimariotis Maria Riga Kostas Haris Konstantinos Toutouzas Aggelos K. Katsaggelos Nicos Maglaveras |
| author_facet | Grigorios-Aris Cheimariotis Maria Riga Kostas Haris Konstantinos Toutouzas Aggelos K. Katsaggelos Nicos Maglaveras |
| author_sort | Grigorios-Aris Cheimariotis |
| collection | DOAJ |
| description | Intravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient’s condition, are better assessed by OCT images in comparison to other in vivo modalities. A large amount of imaging data per patient require automatic methods for rapid results. An effective step towards automatic plaque detection and plaque characterization is axial lines (A-lines) based classification into normal and various plaque types. In this work, a novel automatic method for A-line classification is proposed. The method employed convolutional neural networks (CNNs) for classification in its core and comprised the following pre-processing steps: arterial wall segmentation and an OCT-specific (depth-resolved) transformation and a post-processing step based on the majority of classifications. The important step was the OCT-specific transformation, which was based on the estimation of the attenuation coefficient in every pixel of the OCT image. The dataset used for training and testing consisted of 183 images from 33 patients. In these images, four different plaque types were delineated. The method was evaluated by cross-validation. The mean values of accuracy, sensitivity and specificity were 74.73%, 87.78%, and 61.45%, respectively, when classifying into plaque and normal A-lines. When plaque A-lines were classified into fibrolipidic and fibrocalcific, the overall accuracy was 83.47% for A-lines of OCT-specific transformed images and 74.94% for A-lines of original images. This large improvement in accuracy indicates the advantage of using attenuation coefficients when characterizing plaque types. The proposed automatic deep-learning pipeline constitutes a positive contribution to the accurate classification of A-lines in intravascular OCT images. |
| first_indexed | 2024-03-10T09:02:53Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:02:53Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ef6640bec7064b76870a4e668b31fe772023-11-22T06:41:12ZengMDPI AGApplied Sciences2076-34172021-08-011116741210.3390/app11167412Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation CoefficientsGrigorios-Aris Cheimariotis0Maria Riga1Kostas Haris2Konstantinos Toutouzas3Aggelos K. Katsaggelos4Nicos Maglaveras5Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, Aristotle University, 54124 Thessaloníki, Greece1st Department of Cardiology, National Kapodistrian University of Athens, Hippokration Hospital, 11527 Athens, GreeceLab of Computing, Medical Informatics and Biomedical Imaging Technologies, Aristotle University, 54124 Thessaloníki, Greece1st Department of Cardiology, National Kapodistrian University of Athens, Hippokration Hospital, 11527 Athens, GreeceDepartment of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USALab of Computing, Medical Informatics and Biomedical Imaging Technologies, Aristotle University, 54124 Thessaloníki, GreeceIntravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient’s condition, are better assessed by OCT images in comparison to other in vivo modalities. A large amount of imaging data per patient require automatic methods for rapid results. An effective step towards automatic plaque detection and plaque characterization is axial lines (A-lines) based classification into normal and various plaque types. In this work, a novel automatic method for A-line classification is proposed. The method employed convolutional neural networks (CNNs) for classification in its core and comprised the following pre-processing steps: arterial wall segmentation and an OCT-specific (depth-resolved) transformation and a post-processing step based on the majority of classifications. The important step was the OCT-specific transformation, which was based on the estimation of the attenuation coefficient in every pixel of the OCT image. The dataset used for training and testing consisted of 183 images from 33 patients. In these images, four different plaque types were delineated. The method was evaluated by cross-validation. The mean values of accuracy, sensitivity and specificity were 74.73%, 87.78%, and 61.45%, respectively, when classifying into plaque and normal A-lines. When plaque A-lines were classified into fibrolipidic and fibrocalcific, the overall accuracy was 83.47% for A-lines of OCT-specific transformed images and 74.94% for A-lines of original images. This large improvement in accuracy indicates the advantage of using attenuation coefficients when characterizing plaque types. The proposed automatic deep-learning pipeline constitutes a positive contribution to the accurate classification of A-lines in intravascular OCT images.https://www.mdpi.com/2076-3417/11/16/7412intravascular optical coherence tomographyatheromatic plaquedeep learningCNNclassification |
| spellingShingle | Grigorios-Aris Cheimariotis Maria Riga Kostas Haris Konstantinos Toutouzas Aggelos K. Katsaggelos Nicos Maglaveras Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients Applied Sciences intravascular optical coherence tomography atheromatic plaque deep learning CNN classification |
| title | Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients |
| title_full | Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients |
| title_fullStr | Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients |
| title_full_unstemmed | Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients |
| title_short | Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients |
| title_sort | automatic classification of a lines in intravascular oct images using deep learning and estimation of attenuation coefficients |
| topic | intravascular optical coherence tomography atheromatic plaque deep learning CNN classification |
| url | https://www.mdpi.com/2076-3417/11/16/7412 |
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