Ex vivo brain tumor analysis using spectroscopic optical coherence tomography

Lenz, Marcel; Krug, Robin; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

doi:10.1117/12.2214704

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…ML and DL have also been applied to OCT images of various other tissues. Lenz et al, have demonstrated the application of k-means clustering on short time Fourier transform (STFT) features to detect healthy and malignant brain regions [201]. The texture and optical features of OCT images of human breast tissue at different resolutions were also investigated for the differentiation of major tissue types, such as adipose and malignant lesions.…”

Section: Segmentation and Classification Of Oct Images For Cancer Dia...mentioning

confidence: 99%

Extracting Morphological and Sub-Resolution Features from Optical Coherence Tomography Images, a Review with Applications in Cancer Diagnosis

2023

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Before they become invasive, early cancer cells exhibit specific and characteristic changes that are routinely used by a histopathologist for diagnosis. Currently, these early abnormalities are only detectable ex vivo by histopathology or, non-invasively and in vivo, by optical modalities that have not been clinically implemented due to their complexity and their limited penetration in tissues. Optical coherence tomography (OCT) is a noninvasive medical imaging technology with increasing clinical applications in areas such as ophthalmology, cardiology, gastroenterology, etc. In addition to imaging the tissue micro-structure, OCT can also provide additional information, describing the constituents and state of the cellular components of the tissue. Estimates of the nuclear size, sub-cellular morphological variations, dispersion and index of refraction can be extracted from the OCT images and can serve as diagnostically useful biomarkers. Moreover, the development of fully automated algorithms for tissue segmentation and feature extraction and the application of machine learning, can further enhance the clinical potential of OCT. When fully exploited, OCT has the potential to lead to accurate and sensitive, image-derived, biomarkers for disease diagnosis and treatment monitoring of cancer.

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Section: Segmentation and Classification Of Oct Images For Cancer Dia...mentioning

confidence: 99%

Extracting Morphological and Sub-Resolution Features from Optical Coherence Tomography Images, a Review with Applications in Cancer Diagnosis

2023

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“…In our recent work, we were able to demonstrate that meningioma samples show differences in structure compared to healthy tissue. 15,16 Now we want to enhance these structural variations by employing texture analysis. This postprocessing algorithm is very fast and can be easily implemented in other systems.…”

Section: Introductionmentioning

confidence: 99%

Automated differentiation between meningioma and healthy brain tissue based on optical coherence tomography ex vivo images using texture features

et al. 2018

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Brain tissue analysis is highly desired in neurosurgery, such as tumor resection. To guarantee best life quality afterward, exact navigation within the brain during the surgery is essential. So far, no method has been established that perfectly fulfills this need. Optical coherence tomography (OCT) is a promising three-dimensional imaging tool to support neurosurgical resections. We perform a preliminary study toward in vivo brain tumor removal assistance by investigating meningioma, healthy white, and healthy gray matter. For that purpose, we utilized a commercially available OCT device (Thorlabs Callisto) and measured eight samples of meningioma, three samples of healthy white, and two samples of healthy gray matter ex vivo directly after removal. Structural variations of different tissue types, especially meningioma, can already be seen in the raw OCT images. Nevertheless, an automated differentiation approach is desired, so that neurosurgical guidance can be delivered without a-priori knowledge of the surgeon. Therefore, we employ different algorithms to extract texture features and apply pattern recognition methods for their classification. With these postprocessing steps, an accuracy of nearly 98% was found.

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Ex vivo brain tumor analysis using spectroscopic optical coherence tomography

Cited by 2 publications

References 13 publications

Extracting Morphological and Sub-Resolution Features from Optical Coherence Tomography Images, a Review with Applications in Cancer Diagnosis

Extracting Morphological and Sub-Resolution Features from Optical Coherence Tomography Images, a Review with Applications in Cancer Diagnosis

Automated differentiation between meningioma and healthy brain tissue based on optical coherence tomography ex vivo images using texture features

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