ArticleBMC medical imaging2014
Myofibre segmentation in H&E stained adult skeletal muscle images using coherence-enhancing diffusion filtering.
Article in BMC medical imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- MyoAnalyst: an ImageJ plugin for accurate and automatic myofiber segmentation and analysis in skeletal muscle cross sections.Histochemistry and cell biology · 2025Article
- Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study.Journal of imaging informatics in medicine · 2024Article
- Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle.PloS one · 2020Article
- A neural network approach to analyze cross-sections of muscle fibers in pathological images.Computers in biology and medicine · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe correct segmentation of myofibres in histological muscle biopsy images is a critical step in the automatic analysis process. Errors occurring as a result of incorrect segmentations have a compounding effect on latter morphometric analysis and as such it is vital that the fibres are correctly segmented. This paper presents a new automatic approach to myofibre segmentation in H&E stained adult skeletal muscle images that is based on Coherence-Enhancing Diffusion filtering.
methodsThe procedure can be broadly divided into four steps: 1) pre-processing of the images to extract only the eosinophilic structures, 2) performing of Coherence-Enhancing Diffusion filtering to enhance the myofibre boundaries whilst smoothing the interior regions, 3) morphological filtering to connect unconnected boundary regions and remove noise, and 4) marker controlled watershed transform to split touching fibres.
resultsThe method has been tested on a set of adult cases with a total of 2,832 fibres. Evaluation was done in terms of segmentation accuracy and other clinical metrics.
conclusionsThe results show that the proposed approach achieves a segmentation accuracy of 89% which is a significant improvement over existing methods.
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