The discrimination of tumor-infiltrated tissue from non-tumorous brain tissue during neurosurgical tumor excision is a major challenge in neurosurgery. It is critical to achieve full tumor removal since it directly correlates with the survival rate of the patient. Optical coherence tomography (OCT) might be an additional imaging method in the field of neurosurgery that enables the classification of different levels of tumor infiltration and non-tumorous tissue. This work investigated two OCT systems with different imaging wavelengths (930 nm/1310 nm) and different resolutions (axial (air): 4.9 μm/16 μm, lateral: 5.2 μm/22 μm) in their ability to identify different levels of tumor infiltration based on freshly excised ex vivo brain samples. A convolutional neural network was used for the classification. For both systems, the neural network could achieve classification accuracies above 91% for discriminating between healthy white matter and highly tumor infiltrated white matter (tumor infiltration >60%) .This work shows that both OCT systems with different optical properties achieve similar results regarding the identification of different stages of brain tumor infiltration.
The potential of a new continuous wave Thulium YAG laser is investigated for tissue ablation and cutting focusing on applications in minimally invasive surgery. The laser emits at a wavelength of 2.01µm, which is well suited for tissue ablation due to its high absorption by water. The laser power can be tuned up to 60 W output through a 365 µm core diameter quartz fibre. For the ablation studies, the quartz fibre was placed in contact under various pressures (20 to 90mN) to porcine liver under saline solution in vitro at angles varying between 30° to 60°. The influence of different powers (10 to 60W) and cutting velocities (2 to 10mm/s) on the incision depth and coagulation zones of the tissue were investigated. A maximum incision depth of 3.3 mm was found with a power of 60W, a cutting velocity of 2mm/s and a fibre-tissue angle of 45°. The incisions were surrounded by coagulated tissue between 0.4 and 0.8mm in thickness, sometimes with an inner zone of carbonization of 0.2mm on average. In conclusion, the first experiments show that a cw Thulium laser is very well suited for tissue dissection as required in minimally invasive surgery.
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.
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