Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Jiřík, Miroslav | |
dc.contributor.author | Gruber, Ivan | |
dc.contributor.author | Moulisová, Vladimíra | |
dc.contributor.author | Schindler, Claudia | |
dc.contributor.author | Červenková, Lenka | |
dc.contributor.author | Pálek, Richard | |
dc.contributor.author | Rosendorf, Jáchym | |
dc.contributor.author | Arlt, Janine | |
dc.contributor.author | Bolek, Lukáš | |
dc.contributor.author | Dejmek, Jiří | |
dc.contributor.author | Dahmen, Uta | |
dc.contributor.author | Železný, Miloš | |
dc.contributor.author | Liška, Václav | |
dc.date.accessioned | 2021-03-01T11:00:27Z | - |
dc.date.available | 2021-03-01T11:00:27Z | - |
dc.date.issued | 2020 | |
dc.identifier.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. | cs |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | 2-s2.0-85097520340 | |
dc.identifier.uri | http://hdl.handle.net/11025/42780 | |
dc.format | 12 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | MDPI | en |
dc.relation.ispartofseries | SENSORS | en |
dc.rights | © MDPI | en |
dc.title | Semantic Segmentation of Intralobular and Extralobular Tissue from Liver Scaffold H&E Images | en |
dc.type | článek | cs |
dc.type | article | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | 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. | en |
dc.subject.translated | H&E | en |
dc.subject.translated | decellularization | en |
dc.subject.translated | liver | en |
dc.subject.translated | tissue engineering | en |
dc.subject.translated | semantic segmentation | en |
dc.subject.translated | convolutional neural networks | en |
dc.identifier.doi | 10.3390/s20247063 | |
dc.type.status | Peer-reviewed | en |
dc.identifier.document-number | 603179000001 | |
dc.identifier.obd | 43931735 | |
dc.project.ID | LTARF18017/AMIR - Multimodální rozhraní založené na gestech a mluvené i znakové řeči pro ovládání asistivního mobilního informačního robota | cs |
dc.project.ID | LO1506/PUNTIS - Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost | cs |
dc.project.ID | LM2015042/E-infrastruktura CESNET | cs |
dc.project.ID | 90042/Velká výzkumná infrastruktura povinnost (J) - CESNET II | cs |
dc.project.ID | EF17_048/0007280/Aplikace moderních technologií v medicíně a průmyslu | cs |
Vyskytuje se v kolekcích: | Články / Articles (NTIS) Články / Articles (KKY) OBD |
Soubory připojené k záznamu:
Soubor | Velikost | Formát | |
---|---|---|---|
sensors-20-Jirik_Semantic_Segmentation.pdf | 3,45 MB | Adobe PDF | Zobrazit/otevřít |
Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam:
http://hdl.handle.net/11025/42780
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