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Information × Registration Number 2119U006525, Article popup.category Препринт Title Tissue Segmentation in Histopathological Whole-Slide images with Deep Learning (AI translated) popup.author Pryhoda OleksandrPryhoda Oleksandr popup.publication 01-01-2019 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4476 popup.publisher Description Development of technologies led to the adoption of new digital imaging solutions in pathology field. One such innovation is whole slide imaging, the main purpose of which is digitalizing the whole glass slide with tissue into a high-resolution image. This image is then divided into sections, which are zoomed for further analysis. The main focus of examination is tissue body, but other materials such as debris, dust, and glass are also presented on the slide. In order to focus only on tissue and to make the analysis process more time- and memory-efficient, tissue location on the slide is predefined. Currently, tissue localization procedure is performed by segmentation algorithms based on classical methods of computer vision. These algorithms require manual tuning and might be inaccurate on images with a lot of debris. The issue could be solved with more adaptive methods like deep neural networks. This thesis presents tissue segmentation pipeline based on deep convolutional neural networks. Proposed pipeline showed that deep learning is capable of segmenting tissue as ac- curately as the currently employed approach. popup.nrat_date 2025-11-05 Close