| Autor: |
Arous, Delmon, Schrunner, Stefan, Hanson, Ingunn, Frederike Jeppesen Edin, Nina, Malinen, Eirik |
| Předmět: |
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| Zdroj: |
Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation; Jan2023, Vol. 11 Issue 1, p18-30, 13p |
| Abstrakt: |
Identification, segmentation and counting of stained in vitro cell colonies play a vital part in biological assays. Automating these tasks by optical scanning of cell dishes and subsequent image processing is not trivial due to challenges with, e.g. background noise and contaminations. Here, we present a machine learning procedure to amend these issues by characterising, extracting and segmenting inquired cell colonies using principal component analysis, k -means clustering and a modified watershed segmentation algorithm to automatically identify visible colonies. The proposed segmentation algorithm was tested on two data sets: a T-47D (proprietary) cell colony and a bacteria (open source) data set. High F 1 scores ( ∼ 0.90 for T-47D and > 0.95 for bacterial images), along with low absolute percentage errors ( ∼ 11 % for T-47D and < 5 % for bacterial images), underlined good agreement with ground truth data. Our approach outperformed a recent state-of-the-art method on both data sets, demonstrating the usefulness of the presented algorithm. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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