ArticleBiomedical optics express2023
Deep learning-based optical coherence tomography image analysis of human brain cancer.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- Accurate and timely intraoperative classification of vocal cord leukoplakia by machine learning assisted handheld OCT.Biomedical optics express · 2025Article
- Leveraging pretrained vision transformers for automated cancer diagnosis in optical coherence tomography images.Biomedical optics express · 2025Article
- Optical coherence tomography (OCT) and OCT angiography: Technological development and applications in brain science.Theranostics · 2025Review
- Optical coherence tomography-enabled classification of the human venoatrial junction.Journal of biomedical optics · 2025Article
- Accurate attenuation characterization in optical coherence tomography using multi-reference phantoms and deep learning.Biomedical optics express · 2024Article
- Detection of IDH mutation in glioma by desorption electrospray ionization (DESI) tandem mass spectrometry.Scientific reports · 2024Article
- Applications of Deep Learning Techniques in Healthcare Systems: A Review.Journal of clinical practice and research · 2024Review
- Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Refining neural network algorithms for accurate brain tumor classification in MRI imagery.BMC medical imaging · 2024Article
- Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma.Scientific reports · 2024Article
- Introduction to the Optics and the Brain 2023 feature issue.Biomedical optics express · 2024Article
- Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.Frontiers in computational neuroscience · 2024Article
- The LMIT: Light-mediated minimally-invasive theranostics in oncology.Theranostics · 2024Review
- Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography.Research square · 2023Article
- Advances in computational and translational approaches for malignant glioma.Frontiers in physiology · 2023Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Real-time intraoperative delineation of cancer and non-cancer brain tissues, especially in the eloquent cortex, is critical for thorough cancer resection, lengthening survival, and improving quality of life. Prior studies have established that thresholding optical attenuation values reveals cancer regions with high sensitivity and specificity. However, threshold of a single value disregards local information important to making more robust predictions. Hence, we propose deep convolutional neural networks (CNNs) trained on labeled OCT images and co-occurrence matrix features extracted from these images to synergize attenuation characteristics and texture features. Specifically, we adapt a deep ensemble model trained on 5,831 examples in a training dataset of 7 patients. We obtain 93.31% sensitivity and 97.04% specificity on a holdout set of 4 patients without the need for beam profile normalization using a reference phantom. The segmentation maps produced by parsing the OCT volume and tiling the outputs of our model are in excellent agreement with attenuation mapping-based methods. Our new approach for this important application has considerable implications for clinical translation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.