Tomczyk, ArkadiuszPankiv, OleksandrSzczepaniak, Piotr S.2021-10-252021-10-252021Tomaczyk A., Pankiv O., Szczepaniak Piotr S., Synergy of Convolutional Neural Networks and Geometric Active Contours. W: TEWI 2021 (Technology, Education, Knowledge, Innovation),Wojciechowski A. (Ed.), Napieralski P. (Ed.), Lipiński P. (Ed.)., Seria: Monografie PŁ;Nr 2378, Wydawnictwo Politechniki Łódzkiej, Łódź 2021, s. 207-215, ISBN 978-83-66741-10-2, DOI 10.34658/9788366741102.14.978-83-66741-10-2http://hdl.handle.net/11652/4030https://doi.org/10.34658/9788366741102.14Hybrid approach to machine learning techniques could potentially provide improvements in image segmentation results. In this paper, a model of cooperation of convolutional neural networks and geometric active contours is proposed and developed. The novelty of the approach lies in combining deep neural networks and active contour model in order to improve CNN output results. The method is examined on the image segmentation task and applied to the detection and extraction of nuclei of HL60 cell line. The model had been tested on both 2-D and 3-D images. Because of feature learning characteristics of convolutional neural networks, the proposed solution should perform well in multiple scenarios and can be considered generic.enFair use conditionDla wszystkich w zakresie dozwolonego użytkuconvolutional neural networksCNNgeometric active contoursGACimage segmentationbiomedical applicationsmachine learningsplotowe sieci neuronowegeometryczne aktywne konturysegmentacja obrazuzastosowania biomedyczneuczenie maszynoweRozdział książkiLUT LicenseLicencja PŁ10.34658/9788366741102.14