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Rettig A, Haase T, Pletnyov A, Kohl B, Ertel W, von Kleist M, Sunkara V. SLCV-a supervised learning-computer vision combined strategy for automated muscle fibre detection in cross-sectional images. PeerJ 2019; 7:e7053. [PMID: 31367478 PMCID: PMC6657690 DOI: 10.7717/peerj.7053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/02/2019] [Indexed: 11/20/2022] Open
Abstract
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 brightness and noise in the staining images. In this paper, we introduce the supervised learning-computer vision combined pipeline (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 significantly 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 of 0.9778.
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Affiliation(s)
- Anika Rettig
- Systems Pharmacology and Disease Control, Freie Universität Berlin, Berlin, Germany
| | - Tobias Haase
- Department of Traumatology and Reconstructive Surgery, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Alexandr Pletnyov
- Department of Traumatology and Reconstructive Surgery, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Benjamin Kohl
- Department of Traumatology and Reconstructive Surgery, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Wolfgang Ertel
- Department of Traumatology and Reconstructive Surgery, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Max von Kleist
- Systems Pharmacology and Disease Control, Freie Universität Berlin, Berlin, Germany
| | - Vikram Sunkara
- Systems Pharmacology and Disease Control, Freie Universität Berlin, Berlin, Germany.,Computational Medicine, Zuse Institute Berlin, Berlin, Germany
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