Brain tissue analysis using texture features based on optical coherence tomography images

2018 ◽  
Author(s):  
Martin R. Hofmann ◽  
Marcel Lenz ◽  
Robin Krug ◽  
Christopher Dillmann ◽  
Nils C. Gerhardt ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Brain Tissue ◽  
Texture Features ◽  
Optical Coherence ◽  
Tissue Analysis

scholarly journals Applying machine learning to optical coherence tomography images for automated tissue classification in brain metastases

2021 ◽  
Author(s):  
Jens Möller ◽  
Alexander Bartsch ◽  
Marcel Lenz ◽  
Iris Tischoff ◽  
Robin Krug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Brain Metastases ◽  
Brain Tissue ◽  
Texture Features ◽  
Healthy Tissue ◽  
Support Vector ◽  
Optical Coherence ◽  
Tissue Classification ◽  
Tissue Samples

Abstract Purpose A precise resection of the entire tumor tissue during surgery for brain metastases is essential to reduce local recurrence. Conventional intraoperative imaging techniques all have limitations in detecting tumor remnants. Therefore, there is a need for innovative new imaging methods such as optical coherence tomography (OCT). The purpose of this study is to discriminate brain metastases from healthy brain tissue in an ex vivo setting by applying texture analysis and machine learning algorithms for tissue classification to OCT images. Methods Tumor and healthy tissue samples were collected during resection of brain metastases. Samples were imaged using OCT. Texture features were extracted from B-scans. Then, a machine learning algorithm using principal component analysis (PCA) and support vector machines (SVM) was applied to the OCT scans for classification. As a gold standard, an experienced pathologist examined the tissue samples histologically and determined the percentage of vital tumor, necrosis and healthy tissue of each sample. A total of 14.336 B-scans from 14 tissue samples were included in the classification analysis. Results We were able to discriminate vital tumor from healthy brain tissue with an accuracy of 95.75%. By comparing necrotic tissue and healthy tissue, a classification accuracy of 99.10% was obtained. A generalized classification between brain metastases (vital tumor and necrosis) and healthy tissue was achieved with an accuracy of 96.83%. Conclusions An automated classification of brain metastases and healthy brain tissue is feasible using OCT imaging, extracted texture features and machine learning with PCA and SVM. The established approach can prospectively provide the surgeon with additional information about the tissue, thus optimizing the extent of tumor resection and minimizing the risk of local recurrences.

scholarly journals A Mesh-Based Monte Carlo Study for Investigating Structural and Functional Imaging of Brain Tissue Using Optical Coherence Tomography

2019 ◽  
Vol 9 (19) ◽  
pp. 4008
Author(s):  
Luying Yi ◽  
Liqun Sun ◽  
Mingli Zou ◽  
Bo Hou
Keyword(s):  
Optical Coherence Tomography ◽  
Monte Carlo ◽  
Brain Imaging ◽  
Brain Tissue ◽  
Three Dimensional ◽  
Hemoglobin Concentration ◽  
Monte Carlo Study ◽  
Biological Tissues ◽  
Optical Coherence ◽  
Quantitative Accuracy

Optical coherence tomography (OCT) can obtain high-resolution three-dimensional (3D) structural images of biological tissues, and spectroscopic OCT, which is one of the functional extensions of OCT, can also quantify chromophores of tissues. Due to its unique features, OCT has been increasingly used for brain imaging. To support the development of the simulation and analysis tools on which OCT-based brain imaging depends, a model of mesh-based Monte Carlo for OCT (MMC-OCT) is presented in this work to study OCT signals reflecting the structural and functional activities of brain tissue. In addition, an approach to improve the quantitative accuracy of chromophores in tissue is proposed and validated by MMC-OCT simulations. Specifically, the OCT-based brain structural imaging was first simulated to illustrate and validate the MMC-OCT strategy. We then focused on the influences of different wavelengths on the measurement of hemoglobin concentration C, oxygen saturation Y, and scattering coefficient S in brain tissue. Finally, it is proposed and verified here that the measurement accuracy of C, Y, and S can be improved by selecting appropriate wavelengths for calculation, which contributes to the experimental study of brain functional sensing.

Depth-sensitive Raman spectroscopy combined with optical coherence tomography for layered tissue analysis

2013 ◽  
Vol 7 (1-2) ◽  
pp. 77-85 ◽  
Author(s):  
Khan M. Khan ◽  
Hemant Krishna ◽  
Shovan K. Majumder ◽  
K. Divakar Rao ◽  
Pradeep K. Gupta
Keyword(s):  
Raman Spectroscopy ◽  
Optical Coherence Tomography ◽  
Optical Coherence ◽  
Tissue Analysis

Imaging ex vivo healthy and pathological human brain tissue with ultra-high-resolution optical coherence tomography

2005 ◽  
Vol 10 (1) ◽  
pp. 011006 ◽  
Author(s):  
Kostadinka Bizheva ◽  
Angelika Unterhuber ◽  
Boris Hermann ◽  
Boris Považay ◽  
Harald Sattmann ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
High Resolution ◽  
Human Brain ◽  
Brain Tissue ◽  
Ex Vivo ◽  
Optical Coherence ◽  
Human Brain Tissue
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