An accurate tumor delineation in neurosurgery is still a very challenging problem which we are addressing with optical coherence elastography (OCE). Because of the highly viscoelastic properties of brain tissue, we developed a new Air-Jet based tissue excitation source and evaluated the tissue stiffness with a 3.2 MHz swept-source Optical Coherence Tomography (OCT) system with a line scan rate of 2.45 kHz. The phase based displacement per pixel is measured and stiffness maps are calculated for brain tumor samples. However, certain features in the stiffness maps are seemingly not correlatable to the tissue features in the histological sections. Therefore, the structural properties of the histological sections e.g. fiber orientation, cell nuclei concentration and the “onion structure” with their rotational direction for meningioma were given greater consideration. The structural information are extracted from the histological sections via color deconvolution and structural tensor analysis. First results show that the stiffness transitions correlate with some structures of the histological sections. In summary, the Air-Jet OCE seems to be capable of measuring the stiffness as well as the structural composition of the sample. The long-term aim of this project is to establish OCE to support tumor delineation in the field of neurosurgery.
Microscope integrated real time 4D MHz-OCT operating at high scanning densities are capable of capturing additional visual contrast resolving depth and tissue. Even within a plain C-scan en-face projection structures are recognizable, that are not visible in a white light camera image. With advanced post processing methods, such as absorption coefficient mapping, and morphological classifiers more information is extracted. Presentation to the user in an intuitive way poses practical challenges that go beyond the implementation of a mere overlay display. We present our microscope integrated high speed 4D OCT imaging system, its clinical study use for in-vivo brain tissue imaging, and user feedback on the presentation methods we developed. In neurosurgery the de-facto standard contrast agents used for visibly highlighting brain tumors are Fluorescin and ALA, both of which come with certain caveats. As part of a clinical study we developed a microscope integrated real time 4D MHz-OCT system, operating as high scanning densities, with the intent of creating visual tissue contrast without the use of such contrast agents. Advanced post processing methods to classify tissue can be derived from static properties such as light absorption and morphology, and from dynamic properties, such as perfusion and elastography. However we also noticed that even in a plain C-scan en-face projection structures of interest could be recognized, that were not visible in the corresponding white light camera image. As part of a clinical study so far we collected data from 20 patients, used it for machine learning based classifiers and developing data presentation modalities for eventual use in a surgical environment. We present the challenges in implementing our microscope integrated high speed 4D OCT imaging system, a selection of the imaging data we collected so far during brain tumor surgeries, and the avenues toward presenting processed data to the surgeon.
In recent years, it was demonstrated that discrimination between white matter and tumor-infiltrated white matter based on optical coherence tomography (OCT) data is possible with high accuracy. However, gray matter is also present during the tumor resection and shows similar optical properties to tumor infiltration, which aggravates the tumor classification using optical coherence tomography. A semantic segmentation approach based on a convolutional neural network was applied to the problem in order to classify healthy brain tissue from tumor infiltrated brain tissue. A dataset was created, which consisted of ex vivo OCT B-scans, which were acquired by a swept-source OCT system with a central wavelength of 1300 nm. Each OCT B-scan was indirectly annotated by transforming histological labels from a corresponding H&E section onto it. The labels differentiate between white matter, gray matter and tumor infiltration. The output of the network was modeled to a Dirichlet prior distribution, which enabled the capturing of a prediction uncertainty. This approach achieved an intersection over union score of 0.72 for healthy brain tissue and 0.69 for highly tumor infiltrated brain tissue, when only confident predictions were considered.
Neuro-surgery is challenged by the difficulties of determining brain tumor boundaries during excisions. Optical coherence tomography is investigated as an imaging modality for providing a viable contrast channel. Our MHz-OCT technology enables rapid volumetric imaging, suitable for surgical workflows. We present a surgical microscope integrated MHz-OCT imaging system, which is used for the collection of in-vivo images of human brains, with the purpose of being used in machine learning systems that shall be trained to identify and classify tumorous tissue.
The ill-defined tumor borders of glioblastoma multiforme pose a major challenge for the surgeon during tumor resection, since the goal of the tumor resection is the complete removal, while saving as much healthy brain tissue as possible. In recent years, optical coherence tomography (OCT) was successfully used to classify white matter from tumor infiltrated white matter by several research groups. Motivated by these results, a dataset was created, which consisted of sets of corresponding ex vivo OCT images, which were acquired by two OCT-systems with different properties (e.g. wavelength and resolution). Each image was annotated with semantic labels. The labels differentiate between white and gray matter and three different stages of tumor infiltration. The data from both systems not only allowed a comparison of the ability of a system to identify the different tissue types present during the tumor resection, but also enable a multimodal tissue analysis evaluating corresponding OCT images of the two systems simultaneously. A convolutional neural network with dirichlet prior was trained, which allowed to capture the uncertainty of a prediction. The approach increased the sensitivity of identifying tumor infiltration from 58 % to 78 % for data with a low prediction uncertainty compared to a previous monomodal approach.
The identification of ex vivo brain tumor tissue was investigated with two different optical coherence tomography systems exploiting two optical parameters. The optical parameters were calculated from semantically labelled OCT B-scans.
A 1.6 MHz Fourier-domain mode-locked (FDML) optical coherence tomography (OCT) was adapted to an OR-Microscope for clinical application in neurosurgery. 3D-volume scans at video rate are envisaged with approximately 50μm lateral and 20μm axial resolution
The separation of tumorous brain tissue and healthy brain tissue is still a big challenge in the field of neurosurgery, especially when it comes to the detection of different infiltration grades of glioblastoma multiforme at the tumor border. On the basis of a recently created labelled OCT dataset of ex vivo glioblastoma multiforme tumor samples the detection of brain tumor tissue and the identification of zones with varying degrees of infiltration of tumor cells was investigated. The identification was based on the optical properties, which were extracted by an exponential fit function. The results showed that a separation of tumorous tissue and healthy white matter based on these optical properties is possible. A support vector machine was trained on the optical properties to separate tumor from healthy white matter tissue, which achieved a sensitivity of 91% and a specificity of 76% on an independent training dataset.
Optical coherence tomography (OCT) has the potential to become an additional imaging modality for surgical guidance in the field of neurosurgery, especially when it comes to the detection of different infiltration grades of glioblastoma multiforme at the tumor border. Interpretation of the images, however, is still a big challenge. A method to create a labeled OCT dataset based on ex vivo brain samples is introduced. The tissue samples were embedded in an agarose mold giving them a distinctive shape before images were acquired with two OCT systems (spectral domain (SD) and swept source (SS) OCT) and histological sections were created and segmented by a neuropathologist. Based on the given shape, the corresponding OCT images for each histological image can be determined. The transfer of the labels from the histological images onto the OCT images was done with a non-affine image registration approach based on the tissue shape. It was demonstrated that finding OCT images of a tissue sample corresponding to segmented histological images without any color or laser marking is possible. It was also shown that the set labels can be transferred onto OCT images. The accuracy of method is 26 ± 11 pixel, which translates to 192 ± 75 μm for the SS-OCT and 94 ± 43 μm for the SD-OCT. The dataset consists of several hundred labeled OCT images, which can be used to train a classification algorithm.
The aim of this work is the creation of segmented data set consisting of optical coherence tomography (OCT) scans, which were taken of brain tumor tissue with different tumor infiltration rates. In an ongoing clinical study more than 140 human brain samples with different infiltration grades were recorded ex vivo with two OCT systems, a spectral domain OCT system and a swept-source OCT system that uses a 1310 nm Fourier domain mode locked laser. The histological analysis of the recorded samples builds the ground truth for labeling the corresponding OCT B-Scans. The segmented data set gained from this process will be used to train a classification algorithm, taking into account structural and optical properties such as the attenuation coefficient. In the future the classification algorithm together with a microscope integrated OCT system will be used for the in vivo identification of brain tumors as a guidance tool for the surgeon to increase tumor resection efficiency.
Optical coherence tomography (OCT) is a non-invasive imaging technique which is currently investigated for intraoperative detection of residual tumor during resection of human gliomas. Three different OCT systems were used for imaging of human glioblastoma in vivo (830nm spectral domain (SD) OCT integrated into a surgical microscope) and ex vivo (940nm SD-OCT and 1310nm swept-source MHz-OCT using a Fourier domain mode locked (FDML) laser). Before clinical data acquisition, the systems were characterized using a three-dimensional point-spread function phantom. To distinguish tumor from healthy brain tissue later on, attenuation coefficients of each pixel in OCT depth profiles are calculated. First examples from a clinical study show that the pixel-resolved calculation of the attenuation coefficient provides a good image contrast and confirm that white matter shows a higher signal and more homogeneous signal structure than tumorous tissue.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.