Fully automated segmentation and morphometrical analysis of muscle fibre images

Abstract: Measurement of muscle fibre size and determination of size distribution is important in the assessment of neuromuscular disease. Fibre size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibres in routinely hematoxylin-eosin stained cryostat sections. The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as colour and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts.The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences. The presented region based active contour approach has been proven to accurately segment and measure muscle fibres. Complete automation minimises user interaction, thus, batch processing, as well as objective and reproducible muscle fibre morphometry are provided.