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Muscle segmentation in time series images of Drosophila metamorphosis

机译:果蝇变态的时间序列图像中的肌肉分割

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In order to study genes associated with muscular disorders, we characterize the phenotypic changes in Drosophila muscle cells during metamorphosis caused by genetic perturbations. We collect in vivo images of muscle fibers during remodeling of larval to adult muscles. In this paper, we focus on the new image processing pipeline designed to quantify the changes in shape and size of muscles. We propose a new two-step approach to muscle segmentation in time series images. First, we implement a watershed algorithm to divide the image into edge-preserving regions, and then, we classify these regions into muscle and non-muscle classes on the basis of shape and intensity. The advantage of our method is two-fold: First, better results are obtained because classification of regions is constrained by the shape of muscle cell from previous time point; and secondly, minimal user intervention results in faster processing time. The segmentation results are used to compare the changes in cell size between controls and reduction of the autophagy related gene Atg 9 during Drosophila metamorphosis.
机译:为了研究与肌肉疾病相关的基因,我们表征了在果蝇由遗传扰动引起的变态过程中果蝇肌肉细胞的表型变化。我们收集幼虫到成年肌肉的重塑过程中肌纤维的体内图像。在本文中,我们专注于旨在量化肌肉形状和大小变化的新图像处理管道。我们提出了一种新的两步方法来对时间序列图像中的肌肉进行分割。首先,我们采用分水岭算法将图像划分为边缘保留区域,然后根据形状和强度将这些区域分为肌肉和非肌肉类别。我们方法的优点有两个方面:首先,由于区域的分类受到前一个时间点的肌肉细胞形状的限制,因此获得了更好的结果;其次,最少的用户干预会缩短处理时间。分割结果用于比较果蝇变态过程中对照与自噬相关基因Atg 9减少之间的细胞大小变化。

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