Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber size differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

中文翻译：

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骨骼肌中肌纤维的自动无偏分割和定量

骨骼肌具有显着的再生能力。但是，随着年龄和疾病的发展，肌肉的力量和功能会下降。受损伤和疾病影响的肌纤维大小是评估肌肉健康状况的关键指标。在这里，我们测试并应用Cellpose（一种最近开发的深度学习算法）来自动分割鼠骨骼肌中的肌纤维。我们首先表明，组织固定对于保留细胞结构（如初级纤毛，小细胞触角和脂肪细胞脂质滴）是必要的。但是，固定会产生异质的肌纤维标记，这会阻碍基于强度的分割。我们证明Cellpose有效地描绘了由各种标记勾勒出的成千上万条个体肌纤维，甚至在肌纤维染色高度不均匀的固定组织内。我们创建了一个新颖的ImageJ插件（LabelsToRois），可以批量处理多个Cellpose分割图像。该插件还包含一个半自动腐蚀功能，以校正由不同染色引起的面积偏差，从而像人类专家一样准确地识别肌纤维。我们成功地应用了分割流程，以发现两种不同的肌肉损伤模型（心毒素和甘油）之间的肌纤维大小差异。因此，Cellpose与LabelsToRois结合使用可针对各种染色和固定条件进行快速，无偏且可再现的肌纤维定量。与人类专家一样准确地识别肌纤维。我们成功地应用了分割流程，以发现两种不同的肌肉损伤模型（心毒素和甘油）之间的肌纤维大小差异。因此，Cellpose与LabelsToRois结合使用可针对各种染色和固定条件进行快速，无偏且可再现的肌纤维定量。与人类专家一样准确地识别肌纤维。我们成功地应用了分割流程，以发现两种不同的肌肉损伤模型（心毒素和甘油）之间的肌纤维大小差异。因此，Cellpose与LabelsToRois结合使用可针对各种染色和固定条件进行快速，无偏且可再现的肌纤维定量。