One‐to‐one registration of en‐face optical coherence tomography attenuation coefficients with histology of a prostatectomy specimen

Abstract

Optical coherence tomography (OCT), enables high-resolution 3D imaging of the morphology of light scattering tissues. From the OCT signal, parameters can be extracted and related to tissue structures. One of the quantitative parameters is the attenuation coefficient; the rate at which the intensity of detected light decays in depth. To couple the quantitative parameters with the histology one-to-one registration is needed. The primary aim of this study is to validate a registration method of quantitative OCT parameters to histological tissue outcome through one-to-one registration of OCT with histology. We matched OCT images of unstained fixated prostate tissue slices with corresponding histology slides, wherein different histologic types were demarcated. Attenuation coefficients were determined by a supervised automated exponential fit (corrected for point spread function and sensitivity roll-off related signal losses) over a depth of 0.32 mm starting from 0.10 mm below the automatically detected tissue edge. Finally, the attenuation coefficients corresponding to the different tissue types of the prostate were compared. From the attenuation coefficients, we produced the squared relative residue and goodness-of-fit metric R 2 . This article explains the method to perform supervised automated quantitative analysis of OCT data, and the one-to-one registration of OCT extracted quantitative data with histopathological outcomes. K E Y W O R D Shistopathology, one-to-one registration, optical coherence tomography, prostate

En‐face optical coherence tomography for the detection of cancer in prostatectomy specimens: Quantitative analysis in 20 patients

“…The analysis of the prostatectomy specimens, as depicted in the flowchart in Figure , was carried out following eight previously described steps . These steps are summarized as follows: Step 1.…”

“…The uropathologist identified and demarcated the following tissue types on the histology slides: benign glands, cystic atrophy, inflammation, stroma, Gleason 3, Gleason 4, Gleason 5, fat and regular atrophy. The outlines were filled using a flood fill algorithm using a custom‐made Matlab script (Matlab R2017b, The MathWorks, Inc.) creating regions of homogeneous tissue type . Importantly, for locations where tissue types overlap, the tissue type with the smallest region was kept. Step 4.…”

“…Only good quality histology slides (rated by AS) were accepted, all of which also had a good or medium quality of registration with the OCT mosaic. Step 6. For each 3D OCT dataset, the optical attenuation coefficients ( μ OCT ), residue and R 2 of the fit were automatically determined using a custom‐written code in Matlab (Matlab 7.11.0 R2010b, The Mathworks Inc., Natick, Massachusetts) . All data were automatically straightened to flatten the tissue edge.…”

“…To prevent misregistration at the edges of tissue type, areas were eroded using a “cross”‐mask (allowing only blocks where adjoining horizontal and vertical blocks had the same histology classification). Step 8. Based on previous results , only the data after “cross” erosion of the masks will be used for statistical analysis. A Kruskal Wallis test was performed (using MedCalc Software 15.8, Belgium) to compare the attenuation coefficients of all tissue types, a P ‐value of ≤.05 stated significantly.…”

Angular compounding for speckle reduction in optical coherence tomography using geometric image registration algorithm and digital focusing

“…It has been widely used for diagnosis in ophthalmology 2,3 , with additional diagnostic capabilities for cancer 4 , cardiology 5 , angiography 6 , dentistry 7 , and dermatology 8 . Moreover, it serves as an important tool for brain and neuroscience research 9,10 , shows potential for generating histology-like images 11,12 , and can work with functionalized contrast agents 13,14 . Like other imaging techniques utilizing spatially coherent waves (including ultrasound), OCT images present with a speckled appearance, making visualization of fine structures difficult, and in some cases, impossible, without speckle reduction.…”