Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

SLCV – A Supervised Learning - Computer Vision combined strategy for automated muscle fibre detection in cross sectional images

Please always quote using this URN: urn:nbn:de:0297-zib-72639
  • Muscle fibre cross sectional area (CSA) is an important biomedical measure used to determine the structural composition of skeletal muscle, and it is relevant for tackling research questions in many different fields of research. To date, time consuming and tedious manual delineation of muscle fibres is often used to determine the CSA. Few methods are able to automatically detect muscle fibres in muscle fibre cross sections to quantify CSA due to challenges posed by variation of bright- ness and noise in the staining images. In this paper, we introduce SLCV, a robust semi-automatic pipeline for muscle fibre detection, which combines supervised learning (SL) with computer vision (CV). SLCV is adaptable to different staining methods and is quickly and intuitively tunable by the user. We are the first to perform an error analysis with respect to cell count and area, based on which we compare SLCV to the best purely CV-based pipeline in order to identify the contribution of SL and CV steps to muscle fibre detection. Our results obtained on 27 fluorescence-stained cross sectional images of varying staining quality suggest that combining SL and CV performs signifi- cantly better than both SL based and CV based methods with regards to both the cell separation- and the area reconstruction error. Furthermore, applying SLCV to our test set images yielded fibre detection results of very high quality, with average sensitivity values of 0.93 or higher on different cluster sizes and an average Dice Similarity Coefficient (DSC) of 0.9778.

Download full text files

Export metadata

Author:Anika Rettig, Tobias Haase, Alexandr Pletnyov, Benjamin Kohl, Wolfgang Ertel, Max von Kleist, Vikram SunkaraORCiD
Document Type:Article
Parent Title (English):PeerJ
Place of publication:PeerJ
Year of first publication:2019
Series (Serial Number):ZIB-Report (19-10)
Published in:PeerJ
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.