Abstract
This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 µm thickness. The samples used in this study originate from tumor cell lines characterized as Gli36ϑEGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation.
Highlights
Cancer is one of the most common causes of death worldwide with millions of cases being diagnosed every year
The overall region included in the ground-truth-determined margins is considered as the ground truth binary mask, which was used to calculate the segmentation performance metrics to be depicted in the following subsections
Pixels we consider the pixels of the binary mask extracted via the proposed segmentation pipeline that are active in the ground truth tumor margin region, as true negatives (TN) the pixels of the calculated binary mask that have been successfully classified as non-belonging to the tumorous area, as false positives (FP) the image positions that are active in the estimated tumor margin but are not present in the medical expert’s evaluation mask, and as false negatives (FN) we consider the pixels of the ground truth tumor mask that have not been included in the estimated tumor region
Summary
Cancer is one of the most common causes of death worldwide with millions of cases being diagnosed every year. Therapy and identification of cancer constitute one of the most prominent research fields in the international community, with numerous innovative approaches being developed for early diagnosis and efficient management of the disease. Surgical removal of the primary cancer with or without adjuvant therapy constitutes the standard of care for the disease. To ensure the adequate removal of the cancerous tissue, the protocol requires removing enough normal tissue to achieve a “clean margin” while maintaining the functionality and formation of the organ. Tumor margins are routinely assessed postoperatively for pathological conditions to ensure that the tumor was effectively eliminated
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