Medical Laser Center Luebeck, Luebeck, Germany.
Institute of Biomedical Optics, University of Luebeck, Luebeck, Germany.
Phys Med Biol. 2022 Jun 24;67(13). doi: 10.1088/1361-6560/ac6d9d.
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 timeimaging 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 ± 120m. The resulting dataset consists of 1248 labelled OCT images for each of the two OCT systems.
在肿瘤切除过程中识别肿瘤浸润区域，以便在不损伤健康脑组织的情况下尽可能多地切除肿瘤组织，这仍然是神经外科的一个主要挑战。到目前为止，肿瘤浸润区域的检测需要对预计肿瘤边界处采集的活检样本进行组织学分析。组织学分析的金标准是对薄切标本进行染色，并由神经病理学家进行评估。这项工作提出了一种将神经病理学家的组织学评估转移到光学相干断层扫描（OCT）图像上的方法。OCT 是一种适合神经外科实时成像的方法，但图像需要进行处理以进行肿瘤检测。这里展示的方法能够创建一个数据集，该数据集将用于监督学习，以便为神经外科医生提供更好的肿瘤浸润区域可视化。创建的数据集包含来自两个不同 OCT 系统（波长为 930nm 和 1300nm）的标记 OCT 图像。通过对样本进行塑形、控制切割过程以及基于 OCT 体积的拓扑信息进行刚性变换，确定与染色组织学图像相对应的 OCT 图像。通过基于从组织学图像和 OCT 图像中的样本轮廓中检索到的形状上下文特征的非刚性变换，将组织学标签转移到相应的 OCT 图像上。注册的准确性被确定为 200 ± 120m。结果数据集由两种 OCT 系统中的每个系统的 1248 个标记 OCT 图像组成。
