Deep learning for classification of breast cancer in optical coherence tomography (OCT) imaging (Conference Presentation)

2020 ◽  
Author(s):  
Rohan Bareja ◽  
Diana Mojahed ◽  
Christine P. Hendon
Keyword(s):  
Breast Cancer ◽  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Optical Coherence ◽  
Oct Imaging

scholarly journals Classification of Material Type from Optical Coherence Tomography Images Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Metin Sabuncu ◽  
Hakan Ozdemir
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Fiber Material ◽  
Fiber Types ◽  
Optical Coherence ◽  
Identification Procedure ◽  
Recycling Industry ◽  
Material Type ◽  
Oct Imaging

Classification of material type is crucial in the recycling industry since good quality recycling depends on the successful sorting of various materials. In textiles, the most commonly used fiber material types are wool, cotton, and polyester. When recycling fabrics, it is critical to identify and sort various fiber types quickly and correctly. The standard method of determining fabric fiber material type is the burn test followed by a microscopic examination. This traditional method is destructive, tedious, and slow since it involves cutting, burning, and examining the yarn of the fabric. We demonstrate that the identification procedure can be done nondestructively using optical coherence tomography (OCT) and deep learning. The OCT image scans of fabrics that are composed of different fiber material types such as wool, cotton, and polyester are used to train a deep neural network. We present the results of the created deep learning models’ capability to classify fabric fiber material types. We conclude that fiber material types can be identified nondestructively with high precision and recall by OCT imaging and deep learning. Because classification of material type can be performed by OCT and deep learning, this novel technique can be employed in recycling plants in sorting wool, cotton, and polyester fabrics automatically.

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

scholarly journals Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

2019 ◽  
Vol 178 ◽  
pp. 181-189 ◽  
Author(s):  
Oscar Perdomo ◽  
Hernán Rios ◽  
Francisco J. Rodríguez ◽  
Sebastián Otálora ◽  
Fabrice Meriaudeau ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Approach ◽  
Retinal Diseases ◽  
Optical Coherence

scholarly journals A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

2021 ◽  
Author(s):  
Fangyao Tang ◽  
Xi Wang ◽  
An-ran Ran ◽  
Carmen KM Chan ◽  
Mary Ho ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Macular Edema ◽  
Diabetic Macular Edema ◽  
Vision Loss ◽  
Three Dimensional ◽  
Learning System ◽  
Optical Coherence ◽  
Automated Classification

<a><b>Objective:</b></a> Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep-learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. <p><b>Research Design and Methods:</b> We trained and validated two versions of a multi-task convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume-scans and two-dimensional (2D) B-scans respectively. For both 3D and 2D CNNs, we employed the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent datasets from Singapore, Hong Kong, the US, China, and Australia. </p> <p><b>Results:</b> In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for primary dataset obtained from Cirrus, Spectralis, and Triton OCTs respectively, in addition to AUROCs greater than 0.906 for the external datasets. For the further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary dataset and greater than 0.894 for the external datasets. </p> <p><b>Conclusion:</b> We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics. </p>

scholarly journals Novel Image Features of Optical Coherence Tomography for pathological classification of lung cancer: results from a prospective clinical trial

2021 ◽  
Author(s):  
qiang zhu ◽  
zhen yang ◽  
hang yu ◽  
Zhixin Liang ◽  
wei zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Optical Coherence Tomography ◽  
Optical Coherence ◽  
Clinical Value ◽  
Pathological Classification ◽  
Image Characteristics ◽  
Malignant Lesions ◽  
Oct Imaging ◽  
Oct Image

Abstract Background This study aimed to explore the characteristics of Optical coherence tomography (OCT) imaging for differentiating between benign and malignant lesions, and different pathological types of lung cancer in bronchial lesions, and to preliminarily evaluate the clinical value of OCT. Methods Patients who underwent bronchoscopy biopsy and OCT between February 2019 and December 2019 at the Chinese PLA General Hospital were enrolled in this study. White-light bronchoscopy (WLB), auto-fluorescence bronchoscopy (AFB) and OCT were performed at the lesion location. The main characteristics of OCT imaging for the differentiation between benign and malignant lesions and the prediction of the pathological classification of lung cancer in bronchial lesions were identified and their clinical value was evaluated. Results A total of 135 patients were included in this study. The accuracy of OCT imaging for differentiating between benign and malignant bronchial lesions was 94.1%, which was significantly higher than that of AFB (67.4%). For the OCT imaging of SCC, adenocarcinoma and small cell lung cancer the accuracies were 95.6%, 94.3% and 92%, respectively. The accuracy, sensitivity and specificity of OCT were higher than those of WLB. In addition, these main OCT image characteristics are independent influencing factors for predicting the corresponding diseases through Logistic regression analysis between the main OCT image characteristics in the study and the general clinical features of patients(p༜0.05). Conclusion As a non-biopsy technique, OCT can be used to improve the diagnosis rate of lung cancer and promote the development of non-invasive histological biopsy.

Sign in / Sign up

Export Citation Format

Share Document