Automated image analysis of skeletal muscle fiber cross-sectional area
Automated image analysis of skeletal muscle fiber cross-sectional area
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DOI：
10.1152/japplphysiol.01022.2012
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发表时间：
2013-01-01
影响因子：
3.3
通讯作者：
Peterson, Charlotte A.
中科院分区：
文献类型：
--
作者：
Mula, Jyothi;Lee, Jonah D.;Peterson, Charlotte A.
骨骼肌纤维横截面积的自动图像分析。J Appl Physiol 114：148 - 155，2013.首次发表于2012年11月8日; doi：10.1152/japplphysiol.01022.2012.-肌纤维的形态特征，如纤维大小，是决定肌肉健康和功能的关键因素。然而，此时，肌纤维横截面积的定量仍然是手动的，或者充其量是半自动化的过程。这个过程是劳动密集型的，耗时的，并且容易出错，导致观察者之间的高度差异。我们已经开发并验证了一种自动图像分割算法，并将其直接与目前认为最先进的商用半自动软件进行比较。建议的自动分割算法与手动注释的半自动方法进行了评估，使用35个随机选择的横截面肌肉组织化学图像。该算法首先进行脊检测以增强肌纤维边界，然后进行基于凹区域识别的鲁棒种子检测以找到肌纤维的初始种子。最终的肌纤维边界自动划定使用梯度矢量流变形模型。我们的自动方法是准确的，代表了效率的显著进步;肌肉横截面中纤维面积的量化从25-40分钟/图像减少到15秒/图像，同时适应常见的量化障碍，包括形态学变化（例如，纤维尺寸和纤维化的不均匀性）和技术人工制品（例如，加工缺陷和染色质量差）。使用所提出的方法自动量化的肌纤维横截面积是一个强大的工具，将增加灵敏度，客观性和测量肌肉适应的效率。
Automated image analysis of skeletal muscle fiber cross-sectional area. J Appl Physiol 114: 148 -155, 2013. First published November 8, 2012; doi:10.1152/japplphysiol.01022.2012.-Morphological characteristics of muscle fibers, such as fiber size, are critical factors that determine the health and function of the muscle. However, at this time, quantification of muscle fiber cross-sectional area is still a manual or, at best, a semiautomated process. This process is labor intensive, time consuming, and prone to errors, leading to high interobserver variability. We have developed and validated an automatic image segmentation algorithm and compared it directly with commercially available semiautomatic software currently considered state of the art. The proposed automatic segmentation algorithm was evaluated against a semiautomatic method with manual annotation using 35 randomly selected cross-sectional muscle histochemical images. The proposed algorithm begins with ridge detection to enhance the muscle fiber boundaries, followed by robust seed detection based on concave area identification to find initial seeds for muscle fibers. The final muscle fiber boundaries are automatically delineated using a gradient vector flow deformable model. Our automatic approach is accurate and represents a significant advancement in efficiency; quantification of fiber area in muscle cross sections was reduced from 25-40 min/image to 15 s/image, while accommodating common quantification obstacles including morphological variation (e.g., heterogeneity in fiber size and fibrosis) and technical artifacts (e.g., processing defects and poor staining quality). Automatic quantification of muscle fiber cross-sectional area using the proposed method is a powerful tool that will increase sensitivity, objectivity, and efficiency in measuring muscle adaptation.