Title:	Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images
Authors:	Jiřík, Miroslav Gruber, Ivan Moulisová, Vladimíra Schindler, Claudia Červenková, Lenka Pálek, Richard Rosendorf, Jáchym Arlt, Janine Bolek, Lukáš Dejmek, Jiří Dahmen, Uta Železný, Miloš Liška, Václav
Citation:	JIŘÍK, M., GRUBER, I., MOULISOVÁ, V., SCHINDLER, C., ČERVENKOVÁ, L., PÁLEK, R., ROSENDORF, J., ARLT, J., BOLEK, L., DEJMEK, J., DAHMEN, U., ŽELEZNÝ, M., LIŠKA, V. Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images. Sensors, 2020, roč. 20, č. 24. ISS: 1424-8220.
Issue Date:	2020
Publisher:	MDPI
Document type:	článek article
URI:	2-s2.0-85097520340 http://hdl.handle.net/11025/42780
ISSN:	1424-8220
Keywords in different language:	H&E;decellularization;liver;tissue engineering;semantic segmentation;convolutional neural networks
Abstract in different language:	Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for the assessment of the quality of the liver scaffold without observer bias is the automatic segmentation of the whole slide image into three classes: the background, intralobular area, and extralobular area. Such segmentation enables to perform the texture analysis in the intralobular area of the liver scaffold, which is crucial part in the recellularization procedure. Existing semi-automatic methods for general segmentation (i.e., thresholding, watershed, etc.) do not meet the quality requirements. Moreover, there are no methods available to solve this task automatically. Given the low amount of training data, we proposed a two-stage method. The first stage is based on classification of simple hand-crafted descriptors of the pixels and their neighborhoods. This method is trained on partially annotated data. Its outputs are used for training of the second-stage approach, which is based on a convolutional neural network (CNN). Our architecture inspired by U-Net reaches very promising results, despite a very low amount of the training data. We provide qualitative and quantitative data for both stages. With the best training setup, we reach 90.70% recognition accuracy.
Rights:	© MDPI
Appears in Collections:	Články / Articles (NTIS) Články / Articles (KKY) OBD

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