Characterization of coronary artery pathological formations from OCT imaging using deep learning

Abdolmanafi, Atefeh; Duong, Luc; Dahdah, Nagib; Adib, Ibrahim Ragui; Cheriet, Foued

doi:10.1364/boe.9.004936

Cited by 59 publications

(50 citation statements)

References 44 publications

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“…Unfortunately, it is difficult to compare our results to those from previous studies described in the Introduction [7][8][9][10][11][12][13][14][15][19][20][21][24][25][26] because performance will greatly depend on the case mix and criteria for annotation. Nevertheless, it appears that our method compares favorably to previous studies (Table S1).…”

Section: Discussionmentioning

confidence: 96%

Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

Lee

Prabhu

Kolluru

et al. 2020

Sci Rep

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for intravascular oct (iVoct) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological features. processing was done on innate A-line units with labels fibrolipidic (fibrous tissue followed by lipidous tissue), fibrocalcific (fibrous tissue followed by calcification), or other. We trained/tested on an expansive data set (6,556 images), and performed an active learning, relabeling step to improve noisy ground truth labels. conditional random field was an important post-processing step to reduce classification errors. Sensitivities/ specificities were 84.8%/97.8% and 91.4%/95.7% for fibrolipidic and fibrocalcific plaques, respectively. Over lesions, en face classification maps showed automated results that agreed favorably to manually labeled counterparts. Adding lumen morphological features gave statistically significant improvement (p < 0.05), as compared to classification with convolutional features alone. Automated assessments of clinically relevant plaque attributes (arc angle and length), compared favorably to those from manual labels. our hybrid approach gave statistically improved results as compared to previous A-line classification methods using deep learning or hand-crafted features alone. This plaque characterization approach is fully automated, robust, and promising for live-time treatment planning and research applications. Intravascular optical coherence tomography (IVOCT) is an important technology for planning and assessment of interventional, percutaneous treatments of coronary artery disease. IVOCT is a high contrast, high-resolution imaging modality that uses near-infrared light 1. Compared to intravascular ultrasound (IVUS), this modality provides better image resolution with axial resolution ranging from 12 to 18 μm (as compared to 150-250 μm from IVUS) and lateral resolution ranging from 20 to 90 μm (as compared to 150-300 μm from IVUS) 1. IVOCT allows to determine different plaque components such as fibrous, lipidous, and calcified tissues, and is the only modality that can identify thin cap fibroatheroma 2. IVOCT is used for clinical, live-time intervention planning, and stent deployment assessment. IVOCT-guided percutaneous coronary intervention (PCI) brings valuable benefit for patient treatment as compared to PCI guided by X-ray angiography alone 3. In addition, IVOCT is used for clinical research studies such as the calcium scoring analysis 4 and calcium crack formation 5. Although IVOCT is clearly an excellent method for intravascular imaging of plaque, it has limitations. One is the cost of transducers. Another is tissue penetration depth, especially in the presence of lipidous plaque. Another is the need for a physician trained in visual interpretation of IVOCT images who is willing to take the time to examine images during a stressful procedure. A single IVOCT pullback typically generates 300-500 image frames resulting in data overload. Even ...

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Section: Discussionmentioning

confidence: 96%

Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

Lee

Prabhu

Kolluru

et al. 2020

Sci Rep

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“…Our group compared using a convolutional neural network (CNN) with a fully connected artificial neural network to classify fibrolipidic, fibrocalcific, and other A-lines. 29 Abdolmanafi et al 30 assessed using deep learning methods to perform tissue classification 23 and to identify pathological formations in IVOCT images. Moreover, Gessert et al 31 used CNNs to identify IVOCT frames that contain plaque.…”

Section: Introductionmentioning

confidence: 99%

Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

et al. 2019

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We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous studies (e.g., optical attenuation and A-line peaks). In addition, we included vascular lumen morphology and three-dimensional (3-D) digital edge and texture features. Classification methods were developed using expansive datasets (∼7000 images), consisting of both clinical in-vivo images and an ex-vivo dataset, which was validated using 3-D cryo-imaging/histology. Conditional random field was used to perform 3-D classification noise cleaning of classification results. We tested various multiclass approaches, classifiers, and feature selection schemes and found that a three-class support vector machine with minimal-redundancy-maximal-relevance feature selection gave the best performance. We found that inclusion of our morphological and 3-D features improved overall classification accuracy. On a held-out test set consisting of >1700 images, we obtained an overall accuracy of 81.58%, with the following (sensitivity/ specificity) for each class: other (81.43/89.59), fibrolipidic (94.48/87.32), and fibrocalcific (74.82/95.28). The en-face views of classification results showed that automated classification easily captured the preponderance of a disease segment (e.g., a calcified segment had large regions of fibrocalcific classifications). Finally, we demonstrated proof-of-concept for streamlining A-line classification output with existing fibrolipidic and fibrocalcific boundary segmentation methods, to enable fully automated plaque quantification. The results suggest that our classification approach is a viable step toward fully automated IVOCT plaque classification and segmentation for live-time treatment planning and for offline assessment of drug and biologic therapeutics.

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“…In the experiment of finetuning, classification layers are adapted to their data set, and in the experiments of feature extraction, features are extracted before the last fully connected layer right before the classification layer (pre-trained AlexNet, VGG-19) or before the last depth concatenation layer (mixed10 layer in pre-trained Inception-v3). Experimental results show that finetuning method outperforms the feature extraction method with RF as classifier [24]. Karri et al compares training GoogLeNet from scratch and finetuning pre-trained GoogLeNet on retinal OCT dataset, and their results show that fine-tuned DNN performs better both in convergence speed and classification accuracy [25].…”

Section: Introductionmentioning

confidence: 99%

Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Huang

et al. 2019

Algorithms

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Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our experiments. Results show that optimized deep neural networks not only reduce computational burden, but also improve classification accuracy.

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Characterization of coronary artery pathological formations from OCT imaging using deep learning

Cited by 59 publications

References 44 publications

Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

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