Registration of histological brain images onto optical coherence tomography images based on shape information

Abstract

Identifying tumour infiltration zones during tumour resection in order to excise as much tumour tissue as possible without damaging healthy brain tissue is still a major challenge in neurosurgery. The detection of tumour infiltrated regions so far requires histological analysis of biopsies taken from at expected tumour boundaries. The gold standard for histological analysis is the staining of thin cut specimen and the evaluation by a neuropathologist. This work presents a way to transfer the histological evaluation of a neuropathologist onto optical coherence tomography (OCT) images. OCT is a method suitable for real time in vivo imaging during neurosurgery however the images require processing for the tumour detection. The method demonstrated here enables the creation of a dataset which will be used for supervised learning in order to provide a better visualization of tumour infiltrated areas for the neurosurgeon. The created dataset contains labelled OCT images from two different OCT-systems (wavelength of 930 nm and 1300 nm). OCT images corresponding to the stained histological images were determined by shaping the sample, a controlled cutting process and a rigid transformation process between the OCT volumes based on their topological information. The histological labels were transferred onto the corresponding OCT images through a non-rigid transformation based on shape context features retrieved from the sample outline in the histological image and the OCT image. The accuracy of the registration was determined to be 200 ± 120 µm. The resulting dataset consists of 1248 labelled OCT images for each of the two OCT systems.

Differentiation of different stages of brain tumor infiltration using optical coherence tomography: Comparison of two systems and histology

“…Samples were not taken if the location was too close to a functional area. After the excision, each sample was embedded in an agarose-filled tissue cassette ( Figure 1B ) ( 30 ). The tissue cassette had four imprints of different sizes (3x3x1, 4x4x2, 5x5x3 and 6x6x3 mm 3 ).…”

“…The transfer of the histological information onto the OCT B-scans will only be described briefly. A detailed description of the process can be found in ( 30 ). The first step was to find OCT B-scans that corresponded to the histological section.…”

Dual wavelength analysis and classification of brain tumor tissue with optical coherence tomography

“…The base dataset, which in detail is explained in [7], contains corresponding OCT images acquired by the two OCT systems and a corresponding histological image. The histological image was labeled by a neuropathologist, who assigned labels to healthy white matter, healthy gray matter and three different stages of tumor infiltration (0-30 %, 30-60% and >60%).…”

“…The axial shift was determined by calculating the median difference of the A-scan surface positions between the two corresponding images. The surface position was extracted by using a U-Net, which was introduced in [7] and allowed the separation of tissue information and background. After the transformation images patches were extracted 5 pixels below the surface with a size of 128 pixel x 64 pixel, which translates 320 x 256 µm².…”

“…Over the last decade several research groups demonstrated, that optical coherence tomography (OCT) could be a possible additional imaging method for the detection of the tumor boundaries [3][4][5][6]. In recent publications we introduced a dataset, which consisted of corresponding OCT images from two OCT Systems [7,8]. Detailed histological labels were transformed from histological images onto corresponding OCT images.…”