Registration of histological brain images onto optical coherence tomography images based on shape information. (7th July 2022)

• Record Type: Journal Article
• Authors: Strenge, Paul; Lange, Birgit; Grill, Christin; Draxinger, Wolfgang; Danicke, Veit; Theisen-Kunde, Dirk; Hagel, Christian; Spahr-Hess, Sonja; Bonsanto, Matteo M; Huber, Robert; Handels, Heinz; Brinkmann, Ralf
• Is Part Of: Physics in medicine & biology. Volume 67:Number 13(2022)
• Publication Date: 2022-07-07
• Subjects: brain -- glioblastoma multiforme -- shape -- optical coherence tomography -- OCT; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac6d9d
• Languages: English
• ISSNs: 0031-9155

Abstract

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 μ m. The resulting dataset consists of 1248 labelled OCT images for each of the two OCT systems.