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

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).

Comparison between optical coherence tomography imaging and histological sections of peripheral nerves.

Objective: Optical coherence tomography (OCT) is an imaging technique that uses the light-backscattering properties of different tissue types to generate an image. In an earlier feasibility study the authors showed that it can be applied to visualize human peripheral nerves. As a follow-up, this paper focuses on the interpretation of the images obtained.

High-resolution in vivo imaging of peripheral nerves using optical coherence tomography: a feasibility study.

Objective: Because of their complex topography, long courses, and small diameters, peripheral nerves are challenging structures for radiological diagnostics. However, imaging techniques in the area of peripheral nerve diseases have undergone unexpected development in recent decades. They include MRI and high-resolution sonography (HRS).

Automated differentiation between meningioma and healthy brain tissue based on optical coherence tomography ex vivo images using texture features.

Brain tissue analysis is highly desired in neurosurgery, such as tumor resection. To guarantee best life quality afterward, exact navigation within the brain during the surgery is essential. So far, no method has been established that perfectly fulfills this need.

Multiwavelength phase unwrapping and aberration correction using depth filtered digital holography.

In this Letter, we present a new approach to processing data from a standard spectral domain optical coherence tomography (OCT) system using depth filtered digital holography (DFDH). Intensity-based OCT processing has an axial resolution of the order of a few micrometers. When the phase information is used to obtain optical path length differences, subwavelength accuracy can be achieved, but this limits the resolvable step heights to half of the wavelength of the system.

Comparison of different metrics for analysis and visualization in spectroscopic optical coherence tomography.

Spectroscopic Optical Coherence Tomography (S-OCT) extracts depth resolved spectra that are inherently available from OCT signals. The back scattered spectra contain useful functional information regarding the sample, since the light is altered by wavelength dependent absorption and scattering caused by chromophores and structures of the sample. Two aspects dominate the performance of S-OCT: (1) the spectral analysis processing method used to obtain the spatially-resolved spectroscopic information and (2) the metrics used to visualize and interpret relevant sample features.