3D Optical Coherence Tomography Images Research Articles

Noninvasive virtual biopsy using micro-registered optical coherence tomography (OCT) in human subjects.

Histological hematoxylin and eosin-stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumor margin detection, and disease diagnosis. Producing H&E sections, however, is invasive and time-consuming. While deep learning has shown promise in virtual staining of unstained tissue slides, true virtual biopsy requires staining of images taken from intact tissue. In this work, we developed a micron-accuracy coregistration method [micro-registered optical coherence tomography (OCT)] that can take a two-dimensional (2D) H&E slide and find the exact corresponding section in a 3D OCT image taken from the original fresh tissue. We trained a conditional generative adversarial network using the paired dataset and showed high-fidelity conversion of noninvasive OCT images to virtually stained H&E slices in both 2D and 3D. Applying these trained neural networks to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.

Deep-Learning-Based Group Pointwise Spatial Mapping of Structure to Function in Glaucoma

PurposeTo establish generalizable point-wise spatial relationship between structure and function through occlusion analysis of a deep-learning (DL) model for predicting the visual field (VF) sensitivities from three-dimensional (3D) optical coherence tomography (OCT) scan. DesignRetrospective cross-sectional study. Participants2151 eyes from 1129 patients. MethodsA DL model was trained to predict 52 VF sensitivities of 24-2 standard automated perimetry from 3D spectral-domain OCT images of the optic nerve head (ONH) with 12915 OCT-VF pairs. Using occlusion analysis, the contribution of each individual cube covering a 240 x 240 x 31.25 μm region of the ONH to the model's prediction was systematically evaluated for each OCT-VF pair in a separate test set that consisted of 996 OCT-VF pairs. After simple translation (shifting in x and y-axes to match the ONH center), group t-statistic maps were derived to visualize statistically significant ONH regions for each VF test point within a group. This analysis allowed for understanding the importance of each super voxel (240 x 240 x 31.25 μm covering the entire 4.32 x 4.32 x 1.125 mm ONH cube) in predicting VF test points for specific patient groups. Main Outcome MeasuresThe region at the ONH corresponding to each VF test point and the effect of the former on the latter. ResultsThe test set was divided to two groups, the healthy-to-early-glaucoma group (792 OCT-VF pairs, VF mean deviation (MD): -1.32 ± 1.90 dB) and the moderate-to-advanced-glaucoma group (204 OCT-VF pairs, VF MD: -17.93 ± 7.68 dB). Two-dimensional group t-statistic maps (x, y projection) were generated for both groups, assigning related ONH regions to visual field test points. The identified influential structural locations for VF sensitivity prediction at each test point aligned well with existing knowledge and understanding of structure-function spatial relationships. ConclusionsThis study successfully visualized the global trend of point-by-point spatial relationships between OCT-based structure and VF-based function without the need for prior knowledge or segmentation of OCTs. The revealed spatial correlations were consistent with previously published mappings. This presents possibilities of learning from trained machine learning models without applying any prior knowledge, potentially robust and free from bias.

Robust and accurate corneal interfaces segmentation in 2D and 3D OCT images.

Segmentation of corneal layer interfaces in optical coherence tomography (OCT) images is important for diagnostic and surgical purposes, while manual segmentation is a time-consuming and tedious process. This paper presents a novel technique for the automatic segmentation of corneal layer interfaces using customized initial layer estimation and a gradient-based segmentation method. The proposed method was also extended to three-dimensional OCT images. Validation was performed on two corneal datasets, one with 37 B-scan images of healthy human eyes and the other with a 3D volume scan of a porcine eye. The approach showed robustness in extracting different layer boundaries in the low-SNR region with lower computational cost but higher accuracy compared to existing techniques. It achieved segmentation errors below 2.1 pixels for both the anterior and posterior layer boundaries in terms of mean unsigned surface positioning error for the first dataset and 2.6 pixels (5.2 μm) for segmenting all three layers that can be resolved in the second dataset. On average, it takes 0.7 and 0.4 seconds to process a cross-sectional B-scan image for datasets one and two, respectively. Our comparative study also showed that it outperforms state-of-the-art methods for quantifying layer interfaces in terms of accuracy and time efficiency.

3D Optical Coherence Tomography image processing in BISCAP: characterization of biofilm structure and properties.

BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP's first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d.

Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning.

An integrated cellular-resolution optical coherence tomography (OCT) module with near-infrared Raman spectroscopy was developed on the discrimination of various skin cancer cells and normal cells. Micron-level three-dimensional (3D) spatial resolution and the spectroscopic capability on chemical component determination can be obtained simultaneously. We experimentally verified the effectiveness of morphology, intensity, and spectroscopy features for discriminating skin cells. Both spatial and spectroscopic features were employed for the discrimination of five types of skin cells, including keratinocytes (HaCaT), the cell line of squamous cell carcinoma (A431), the cell line of basal cell carcinoma (BCC-1/KMC), primary melanocytes, and the cell line of melanoma (A375). The cell volume, compactness, surface roughness, average intensity, and internal intensity standard deviation were extracted from the 3D OCT images. After removing the fluorescence components from the acquired Raman spectra, the entire spectra (600 to ) were used. An accuracy of 85% in classifying five types of skin cells was achieved. The cellular-resolution OCT images effectively differentiate cancer and normal cells, whereas Raman spectroscopy can distinguish the cancer cells with nearly 100% accuracy. Among the OCT image features, cell surface roughness, internal average intensity, and standard deviation of internal intensity distribution effectively differentiate the cancerous and normal cells. The three features also worked well in sorting the keratinocyte and melanocyte. Using the full Raman spectra, the melanoma and keratinocyte-based cell carcinoma cancer cells can be discriminated effectively.

Rapid Quantification of Microvessels of Three-Dimensional Blood-Brain Barrier Model Using Optical Coherence Tomography and Deep Learning Algorithm.

The blood-brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models.

Automated robot-assisted wide-field optical coherence tomography using structured light camera.

Optical coherence tomography (OCT) is a promising real-time and non-invasive imaging technology widely utilized in biomedical and material inspection domains. However, limited field of view (FOV) in conventional OCT systems hampers their broader applicability. Here, we propose an automated system integrating a structured light camera and robotic arm for large-area OCT scanning. The system precisely detects tissue contours, automates scan path generation, and enables accurate scanning of expansive sample areas. The proposed system consists of a robotic arm, a three-dimensional (3D) structured light camera, and a customized portable OCT probe. The 3D structured light camera is employed to generate a precise 3D point cloud of the sample surface, enabling automatic planning of the scanning path for the robotic arm. Meanwhile, the OCT probe is mounted on the robotic arm, facilitating scanning of the sample along the predetermined path. Continuous OCT B-scans are acquired during the scanning process, facilitating the generation of high-resolution and large-area 3D OCT reconstructions of the sample. We conducted position error tests and presented examples of 3D macroscopic imaging of different samples, such as ex vivo kidney, skin and leaf blade. The robotic arm can accurately reach the planned positions with an average absolute error of approximately 0.16 mm. The findings demonstrate that the proposed system enables the acquisition of 3D OCT images covering an area exceeding 20 cm2, indicating wide-ranging potential for utilization in diverse domains such as biomedical, industrial, and agricultural fields.

Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation.

Optical coherence tomography (OCT) is the most widely used imaging modality in ophthalmology. There are multiple variations of OCT imaging capable of producing complementary information. Thus, registering these complementary volumes is desirable in order to combine their information. In this work, we propose a novel automated pipeline to register OCT images produced by different devices. This pipeline is based on two steps: a multi-modal 2D en-face registration based on deep learning, and a Z-axis (axial axis) registration based on the retinal layer segmentation. We evaluate our method using data from a Heidelberg Spectralis and an experimental PS-OCT device. The empirical results demonstrated high-quality registrations, with mean errors of approximately 46 µm for the 2D registration and 9.59 µm for the Z-axis registration. These registrations may help in multiple clinical applications such as the validation of layer segmentations among others.

Low tensor train and low multilinear rank approximations of 3D tensors for compression and de-speckling of optical coherence tomography images

Objective. Many methods for compression and/or de-speckling of 3D optical coherence tomography (OCT) images operate on a slice-by-slice basis and, consequently, ignore spatial relations between the B-scans. Thus, we develop compression ratio (CR)-constrained low tensor train (TT)—and low multilinear (ML) rank approximations of 3D tensors for compression and de-speckling of 3D OCT images. Due to inherent denoising mechanism of low-rank approximation, compressed image is often even of better quality than the raw image it is based on. Approach. We formulate CR-constrained low rank approximations of 3D tensor as parallel non-convex non-smooth optimization problems implemented by alternating direction method of multipliers of unfolded tensors. In contrast to patch- and sparsity-based OCT image compression methods, proposed approach does not require clean images for dictionary learning, enables CR as high as 60:1, and it is fast. In contrast to deep networks based OCT image compression, proposed approach is training free and does not require any supervised data pre-processing. Main results. Proposed methodology is evaluated on twenty four images of a retina acquired on Topcon 3D OCT-1000 scanner, and twenty images of a retina acquired on Big Vision BV1000 3D OCT scanner. For the first dataset, statistical significance analysis shows that for CR ≤ 35, all low ML rank approximations and Schatten-0 (S 0) norm constrained low TT rank approximation can be useful for machine learning-based diagnostics by using segmented retina layers. Also for CR ≤ 35, S 0-constrained ML rank approximation and S 0-constrained low TT rank approximation can be useful for visual inspection-based diagnostics. For the second dataset, statistical significance analysis shows that for CR ≤ 60 all low ML rank approximations as well as S 0 and S 1/2 low TT ranks approximations can be useful for machine learning-based diagnostics by using segmented retina layers. Also, for CR ≤ 60, low ML rank approximations constrained with S p , p ∊ {0, 1/2, 2/3} and one surrogate of S 0 can be useful for visual inspection-based diagnostics. That is also true for low TT rank approximations constrained with S p , p ∊ {0, 1/2, 2/3} for CR ≤ 20. Significance. Studies conducted on datasets acquired by two different types of scanners confirmed capabilities of proposed framework that, for a wide range of CRs, yields de-speckled 3D OCT images suitable for clinical data archiving and remote consultation, for visual inspection-based diagnosis and for machine learning-based diagnosis by using segmented retina layers.

Effect of an Anaerobic Fermentation Process on 3D-Printed PLA Materials of a Biogas-Generating Reactor.

3D-printed materials are present in numerous applications, from medicine to engineering. The aim of this study is to assess their suitability for an application of interest today, that of testing of 3D-printed polylactic acid (PLA)-based reactors for biogas production using anaerobic digestion. The impact of temperature, pH, and aqueous phase on the tested bioreactor is investigated, together with the effect of the gaseous phase (i.e., produced biogas). Two batches of materials used separately, one after another inside the bioreactor were considered, in a realistic situation. Two essential parameters inside the reactor (i.e., pH and temperature) were continuously monitored during a time interval of 25 to 30 days for each of the two biogas-generating processes. To understand the impact of these processes on the walls of the bioreactor, samples of 3D-printed material were placed at three levels: at the top (i.e., outside the substrate), in the middle, and at the bottom of the bioreactor. The samples were analyzed using a non-destructive imaging method, Optical Coherence Tomography (OCT). An in-house developed swept-source (SS) OCT system, master-slave (MS) enhanced, operating at a central wavelength of 1310 nm was utilized. The 3D OCT images related to the degradation level of the material of the PLA samples were validated using Scanning Electron Microscopy (SEM). The differences between the impact of the substrate on samples situated at the three considered levels inside the reactor were determined and analyzed using their OCT B-scans (optical cross-section images). Thus, the impact of the biogas-generating process on the interior of the bioreactor was demonstrated and quantified, as well as the capability of OCT to perform such assessments. Therefore, future work may target OCT for in situ investigations of such bioreactors.

The Use of 3D Optical Coherence Tomography to Analyze the Architecture of Cyanobacterial Biofilms Formed on a Carbon Nanotube Composite.

The development of environmentally friendly antifouling strategies for marine applications is of paramount importance, and the fabrication of innovative nanocomposite coatings is a promising approach. Moreover, since Optical Coherence Tomography (OCT) is a powerful imaging technique in biofilm science, the improvement of its analytical power is required to better evaluate the biofilm structure under different scenarios. In this study, the effect of carbon nanotube (CNT)-modified surfaces in cyanobacterial biofilm development was assessed over a long-term assay under controlled hydrodynamic conditions. Their impact on the cyanobacterial biofilm architecture was evaluated by novel parameters obtained from three-dimensional (3D) OCT analysis, such as the contour coefficient, total biofilm volume, biovolume, volume of non-connected pores, and the average size of non-connected pores. The results showed that CNTs incorporated into a commercially used epoxy resin (CNT composite) had a higher antifouling effect at the biofilm maturation stage compared to pristine epoxy resin. Along with a delay in biofilm development, a decrease in biofilm wet weight, thickness, and biovolume was also achieved with the CNT composite compared to epoxy resin and glass (control surfaces). Additionally, biofilms developed on the CNT composite were smoother and presented a lower porosity and a strictly packed structure when compared with those formed on the control surfaces. The novel biofilm parameters obtained from 3D OCT imaging are extremely important when evaluating the biofilm architecture and behavior under different scenarios beyond marine applications.

Modifications of the Dental Hard Tissues in the Cervical Area of Occlusally Overloaded Teeth Identified Using Optical Coherence Tomography.

Background and objectives: Occlusal overloads produce a series of manifestations in teeth, especially attrition and non-carious cervical lesions (NCCL). Optical Coherence Tomography (OCT) can highlight and evaluate tooth lesions. The aim of this study was to examine the changes of dental hard tissues in the cervical area because of occlusal overload by macroscopic examination and using in vitro Swept Source OCT examination. Materials and Methods: The study was performed on 21 extracted teeth with occlusal trauma. After macroscopic and OCT examination, the 2D OCT images were transformed into 3D images using ImageJ software. Statistical analysis of macroscopic and OCT images was performed with Statistical Package for Social Sciences. Results: On 21 teeth, 88 cervical lesions (cracks) were identified. Upper premolars with an occlusal Smith and Knight tooth wear score of 2 had the most NCCL. Statistical analysis revealed significant differences in the median widths of cervical lesions between teeth with score 1 and score 3. Additionally, we obtained statistically significant differences in median widths between the buccal and oral surfaces. Conclusions: These cracks can be considered precursors of NCCL. NCCL can be located on dental surfaces in the cervical area other than the buccal one. A 3D reconstruction of OCT images emphasized that cracks are located especially at enamel level, evolving towards the enamel–dentin junction, with multiple ramifications.

Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia.

Pathologic myopia causes vision impairment and blindness, and therefore, necessitates a prompt diagnosis. However, there is no standardized definition of pathologic myopia, and its interpretation by 3D optical coherence tomography images is subjective, requiring considerable time and money. Therefore, there is a need for a diagnostic tool that can automatically and quickly diagnose pathologic myopia in patients. This study aimed to develop an algorithm that uses 3D optical coherence tomography volumetric images (C-scan) to automatically diagnose patients with pathologic myopia. The study was conducted using 367 eyes of patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary’s Hospital and Seoul St. Mary’s Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. The model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). Grad-CAM was used to visualize features affecting the detection of pathologic myopia. The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC) in identifying pathologic myopia.

Characterization of biofilm structure and properties via processing of 2D optical coherence tomography images in BISCAP.

Processing of Optical Coherence Tomography (OCT) biofilm images is currently restricted to a set of custom-made MATLAB scripts. None of the tools currently available for biofilm image processing (including those developed for Confocal Laser Scanning Microscopy-CLSM) enable a fully automatic processing of 2D OCT images. A novel software tool entitled Biofilm Imaging and Structure Classification Automatic Processor (BISCAP) is presented. It was developed specifically for the automatic processing of 2D OCT biofilm images. The proposed approach makes use of some of the key principles used in CLSM image processing, and introduces a novel thresholding algorithm and substratum detection strategy. Two complementary pixel continuity checks are executed, enabling very detailed pixel characterizations. BISCAP delivers common structural biofilm parameters and a set of processed images for biofilm analysis. A novel biofilm 'compaction parameter' is suggested. The proposed strategy was tested on a set of 300 images with highly satisfactory results obtained. BISCAP is a Python-based standalone application, not requiring any programming knowledge or property licenses, and where all operations are managed via an intuitive Graphical User Interface. The automatic nature of this image processing strategy decreases biasing problems associated to human-perception and allows a reliable comparison of outputs. BISCAP and a collection of biofilm images obtained from OCT scans can be found at: https://github.com/diogonarciso/BISCAP. Supplementary data are available at Bioinformatics online.

Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images.

The choroid is the vascular layer of the eye that supplies photoreceptors with oxygen. Changes in the choroid are associated with many pathologies including myopia where the choroid progressively thins due to axial elongation. To quantize these changes, there is a need to automatically and accurately segment the choroidal layer from optical coherence tomography (OCT) images. In this paper, we propose a multi-task learning approach to segment the choroid from three-dimensional OCT images. Our proposed architecture aggregates the spatial context from adjacent cross-sectional slices to reconstruct the central slice. Spatial context learned by this reconstruction mechanism is then fused with a U-Net based architecture for segmentation. The proposed approach was evaluated on volumetric OCT scans of 166 myopic eyes acquired with a commercial OCT system, and achieved a cross-validation Intersection over Union (IoU) score of 94.69% which significantly outperformed (p<0.001) the other state-of-the-art methods on the same data set. Choroidal thickness maps generated by our approach also achieved a better structural similarity index (SSIM) of 72.11% with respect to the groundtruth. In particular, our approach performs well for highly challenging eyes with thinner choroids. Compared to other methods, our proposed approach also requires lesser processing time and has lower computational requirements. The results suggest that our proposed approach could potentially be used as a fast and reliable method for automated choroidal segmentation.

26738 Assessing the cosmetic changes in the facial skin using noninvasive RCM and OCT in vivo skin imaging instrumentation after use of a topical retinoid product in subjects with moderate to severe photodamage

There are limited studies assessing cosmetic skin changes using reflectance confocal microscopy (RCM) and optical coherence tomography (OCT). To explore the cosmetic effects of a topical cosmetic retinoid (RET05) a study was conducted using RCM and OCT. A twelve-week, open-label study was conducted to assess the effects of RET05 on subjects with facial photodamage. Study endpoints included investigator grading, standardized (VISIA-CR) and 3D Photography (Antera3D), independent RCM (VivaScope1500) and OCT (VivoSight) image analysis, validated FACE-Q scales, and subject questionnaires.

Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation

Optical coherence tomography (OCT) images of the retina provide a structural representation and give an insight into the pathological changes present in age-related macular degeneration (AMD). Due to the three-dimensionality and complexity of the images, manual analysis of pathological features is difficult, time-consuming, and prone to subjectivity. Computer analysis of 3D OCT images is necessary to enable automated quantitative measuring of the features, objectively and repeatedly. As supervised and semi-supervised learning-based automatic segmentation depends on the training data and quality of annotations, we have created a new database of annotated retinal OCT images – the AROI database. It consists of 1136 images with annotations for pathological changes (fluid accumulation and related findings) and basic structures (layers) in patients with AMD. Inter- and intra-observer errors have been calculated in order to enable the validation of developed algorithms in relation to human variability. Also, we have performed the automatic segmentation with standard U-net architecture and two state-of-the-art architectures for medical image segmentation to set a baseline for further algorithm development and to get insight into challenges for automatic segmentation. To facilitate and encourage further research in the field, we have made the AROI database openly available.

Robotic-arm-assisted flexible large field-of-view optical coherence tomography

Optical coherence tomography (OCT) is a three-dimensional non-invasive high-resolution imaging modality that has been widely used for applications ranging from medical diagnosis to industrial inspection. Common OCT systems are equipped with limited field-of-view (FOV) in both the axial depth direction (a few millimeters) and lateral direction (a few centimeters), prohibiting their applications for samples with large and irregular surface profiles. Image stitching techniques exist but are often limited to at most 3 degrees-of-freedom (DOF) scanning. In this work, we propose a robotic-arm-assisted OCT system with 7 DOF for flexible large FOV 3D imaging. The system consists of a depth camera, a robotic arm and a miniature OCT probe with an integrated RGB camera. The depth camera is used to get the spatial information of targeted sample at large scale while the RGB camera is used to obtain the exact position of target to align the image probe. Eventually, the real-time 3D OCT imaging is used to resolve the relative pose of the probe to the sample and as a feedback for imaging pose optimization when necessary. Flexible probe pose manipulation is enabled by the 7 DOF robotic arm. We demonstrate a prototype system and present experimental results with flexible tens of times enlarged FOV for plastic tube, phantom human finger, and letter stamps. It is expected that robotic-arm-assisted flexible large FOV OCT imaging will benefit a wide range of biomedical, industrial and other scientific applications.

Assessment of single and double coronary bifurcation stenting techniques using multimodal imaging and 3D modeling in reanimated swine hearts using Visible Heart\xae methodologies

Stent implantation in bifurcated coronary lesions is technically challenging so that procedural refinements are continuously investigated. Novel procedure modeling and intracoronary imaging techniques may offer critical insights on stent deformations and stent-wall interactions during bifurcation stenting procedures. Thus, we assessed coronary bifurcation stenting techniques using multimodal imaging and 3D modeling in reanimated swine hearts. Harvested swine hearts were reanimated using Visible Heart® methodologies and (under standard fluoroscopic guidance) used to test 1-stent (provisional and inverted provisional) and 2-stent (culotte, TAP and DK-crush) techniques on bifurcations within various coronary vessels using commercially available devices. Intracoronary angioscopy and frequency-domain optical-coherence-tomography (OCT) were obtained during the procedures. 3D OCT reconstruction and micro-computed tomography 3D modeling (post heart fixations) were used to assess stent deformations and stent-wall interactions. We conducted multiple stenting procedures and collected unique endoscopic and OCT images (and subsequent computational models from micro-CT) to assess stent deformations and device/wall interactions during different steps of bifurcation stenting procedures. Endoscopy, micro-CT and virtual reality processing documented that different 1- and 2-stent techniques, practiced according to experts’ recommended steps, achieve optimal post-intervention stent conformation. As compared with intra-procedural endoscopy, software-generated 3D OCT images accurately depicted stent deformations during 1-stent techniques. On the opposite, during more complex 2-stent techniques, some defects were appreciated at 3D OCT reconstruction despite optimal 2D OCT images. This study provided unique insights regarding both stent deformations occurring in the course of bifurcation stenting and the efficacy of OCT to visualize them.

Quantitative characterization of human breast tissue based on deep learning segmentation of 3D optical coherence tomography images.

In this study, we performed dual-modality optical coherence tomography (OCT) characterization (volumetric OCT imaging and quantitative optical coherence elastography) on human breast tissue specimens. We trained and validated a U-Net for automatic image segmentation. Our results demonstrated that U-Net segmentation can be used to assist clinical diagnosis for breast cancer, and is a powerful enabling tool to advance our understanding of the characteristics for breast tissue. Based on the results obtained from U-Net segmentation of 3D OCT images, we demonstrated significant morphological heterogeneity in small breast specimens acquired through diagnostic biopsy. We also found that breast specimens affected by different pathologies had different structural characteristics. By correlating U-Net analysis of structural OCT images with mechanical measurement provided by quantitative optical coherence elastography, we showed that the change of mechanical properties in breast tissue is not directly due to the change in the amount of dense or porous tissue.