Szczegóły publikacji
Opis bibliograficzny
Robust multiresolution and multistain background segmentation in whole slide images / Artur Jurgas, Marek WODZIŃSKI, Manfredo Atzori, Henning Müller // W: The latest developments and challenges in biomedical engineering : proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering : Lodz, Poland, September 27–29, 2023 / eds. Paweł Strumiłło, [et al.]. — Cham : Springer Nature, cop. 2024. — (Lecture Notes in Networks and Systems ; ISSN 2367-3370 ; LNNS 746). — ISBN: 978-3-031-38429-5; e-ISBN: 978-3-031-38430-1. — S. 29–40. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-09-11. — A. Jurgas, M. Wodziński - dod. afiliacja: University of Applied Sciences Western Switzerland (HES-SO Valais), Information Systems Institute, Sierre, Switzerland
Autorzy (4)
- AGHJurgas Artur
- AGHWodziński Marek
- Atzori Manfredo
- Müller Henning
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 148907 |
|---|---|
| Data dodania do BaDAP | 2023-10-20 |
| DOI | 10.1007/978-3-031-38430-1_3 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
Abstract
Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research.
