Instance Segmentation Model Created from Three Semantic Segmentations of Mask, Boundary and Centroid Pixels Verified on GlaS Dataset

• Autorzy: Malík Peter , Krištofík Štefan , Knapová Kristína

Identyfikatory

DOI

10.15439/2020F175

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Segmentation is the key computer vision task in modern medicine applications. Instance segmentation became the prevalent way to improve segmentation performance in recent years. This work proposes a novel way to design an instance segmentation model that combines 3 semantic segmentation models dedicated for foreground, boundary and centroid predictions. It contains no detector so it is orthogonal to a standard instance segmentation design and can be used to improve the performance of a standard design. The presented custom designed model is verified on the Gland Segmentation in Colon Histology Images dataset.

Słowa kluczowe

EN

biological organs; biological tissue; biomedical optical imaging; computer vision; image classification; image segmentation; medical image processing

PL

narząd biologiczny; tkanka biologiczna; biomedyczne obrazowanie optyczne; wizja komputerowa; klasyfikacja obrazu; segmentacja obrazu; przetwarzanie obrazu medycznego

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 569--576
• Opis fizyczny: Bibliogr. 27 poz., tab., wykr., il.

Twórcy

autor

Malík Peter

• Institute of Informatics, Slovak Academy of Sciences, Dúbravská cesta 9, 845 07 Bratislava, Slovakia

autor

Krištofík Štefan

• Institute of Informatics, Slovak Academy of Sciences, Dúbravská cesta 9, 845 07 Bratislava, Slovakia

autor

Knapová Kristína

• Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovičova 2, 842 16 Bratislava, Slovakia

Bibliografia

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Uwagi

1. Track 4: Information Systems and Technology

2. Technical Session: 2nd Special Session on Data Science in Health, Ecology and Commerce

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).