Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

Meiburger, Kristen M.; Salvi, Massimo; Rotunno, Giulia; Drexler, Wolfgang; Li, Mengyang

doi:10.3390/app11209734

Cited by 17 publications

(5 citation statements)

References 100 publications

(164 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, all methods require manual tuning and suffer from poor robustness towards the diverse set of image artifacts in real OCTA data. Furthermore, small vessels such as the capillaries from the deep vascular complex (DVC) are hard to detect using thresholding but crucial for early disease detection [6].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations

Kreitner,

Paetzold,

Rauch

et al. 2024

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.

show abstract

Section: Introductionmentioning

confidence: 99%

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations

Kreitner,

Paetzold,

Rauch

et al. 2024

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis, as it can offer high-resolution, label-free, non-destructive, and real-time 3D imaging of biological tissues [ 13 , 14 ] and vasculature if multiple volumes are acquired at the same location through OCT angiography [ 15 ]. The label-free and non-destructive nature of OCT is pivotal as it permits the longitudinal monitoring of organoids, allowing a quantitative analysis of numerous important features of single organoid growth and status, such as volume and internal structures [ 8 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks

Branciforti,

Salvi,

D’Agostino

et al. 2024

Diagnostics

Self Cite

View full text Add to dashboard Cite

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

show abstract

“… 7 Reported methods for CNV classification/segmentation rely on indocyanine green/fluorescein angiography (ICGA/FA), optical coherence tomography (OCT), and/or OCT angiography (OCTA). 8 – 22 Limitations for CNV analysis using FA and ICGA include 9 – 12 (1) a restriction to two-dimensional (2D) imaging that projects three-dimensional (3D) features onto a 2D plane; (2) detection reliant on the appearance of hyperfluorescence caused by CNV vessel leakage, which obscures CNV vessels at the capillary level; and (3) a limited ability to diagnose and characterize nonexudative lesions. A final concern is that as invasive techniques, ICGA and FA are ill-suited for routine examination and screening.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set

Wang

Hormel

Tsuboi

et al. 2023

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

Purpose To diagnose and segment choroidal neovascularization (CNV) in a real-world multicenter clinical OCT angiography (OCTA) data set using deep learning. Methods A total of 105,66 OCTA scans from 3135 eyes, including 4701 with CNV and 5865 without, were collected in five eye clinics. Both 3 × 3-mm and 6 × 6-mm scans of the central and temporal macula were included. Scans with CNV were collected from multiple diseases, and scans without CNV were collected from both healthy controls and those with multiple diseases. No scans were removed during training or testing due to poor quality. The trained hybrid multitask convolutional neural network outputs a CNV diagnosis and membrane segmentation, respectively. Results The model demonstrated a highly accurate CNV diagnosis (area under receiver operating characteristic curve = 0.97), achieving a sensitivity of 95% at 95% specificity. The model also correctly segmented CNV lesions (F1 score = 0.78 ± 0.19). Additionally, model performance was comparable on both high-definition 3 × 3-mm scans and low-definition 6 × 6-mm scans. The model did not suffer large performance variations under different diseases. We also show that a subclinical lesion in a patient with neovascular age-related macular degeneration can be monitored over a multiyear time frame using our approach. Conclusions The proposed method can accurately diagnose and segment CNV in a large real-world clinical data set. Translational Relevance The algorithm could enable automated CNV screening and quantification in the clinic, which will help improve CNV diagnosis and treatment evaluation.

show abstract

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

Cited by 17 publications

References 100 publications

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations

Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks

Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set

Contact Info

Product

Resources

About