# Unsupervised Segmentation of Muscle Precursor Cell Images In Situ
## 1 Introduction  
Muscle is an important motor and metabolic component in the human body, but there is no viable treatment for muscular degeneration caused by aging and disease, as well as muscle atrophy induced by long-term spaceflight in a weightless environment [1]. Alternatively, cell replacement therapy may be a breakthrough that injects healthy cells into the injury to replace dead cells to repair the injured tissue and restore the lost function [2, 3]. The premise of the therapy is to obtain a large number of functional cells required for transplantation. The in vitro expansion technology of adult stem cells has been relatively mature in biological laboratories [4, 5, 6]. Cell culture depends greatly on manual experience, subject to subjective facts: The quality of cells cultured by different experimenters varies widely and the same experimenter's judgement also varies with time. Manual control alone cannot achieve refined control in mass production. Therefore, the automatic culture of muscle stem cells in vitro by microscopy and computer technology must be considered for industrialization. The most common method for gaining a suitable live cell image is fluorescence imaging which uses markers to label cells and capture the specific fluorescence for imaging. [PERSON] et al. [7, 8] have performed nuclear and cytoplasmic segmentation on the fluorescently stained images of smear muscle fibers by the threshold segmentation algorithm. However, fluorescence imaging also has conspicuous shortcomings: the dye is toxic and the fluorescence has a quenching time for that long-term observation cannot be guaranteed. Hence it is necessary to resort to a safe and long-lasting imaging method for long-term live cell monitoring. It is critical to judge the cell culture state by computer measurement of cell morphological parameters like cell fusion length, and diameter after receiving the images. Thus many open-source cell image processing tools have been developed like CellProfiler [9], Image J [10], Cytok [11] and most of them integrate cell segmentation functionality because the result of image segmentation directly affects the accuracy of measurement. The segmentation algorithms of these tools typically call packages from algorithm libraries such as OpenCV and SciPy. The current mainstream cell image segmentation algorithms can be divided into two major categories: one is based on heuristic image algorithms such as watershed, region-growing, level-set, etc. [12; 13; 14; 15]. Since these algorithms perform well on regularly shaped circular and elliptical cells as well as nuclei segmentation. Users often need to continuously adjust parameters for specific images and the segmentation performance for irregular and overlapping cells may not always meet expectations. The outstanding semantic segmentation algorithms typically belong to another category: neural network-based image segmentation algorithms [16], many of which are primarily based on convolutional neural networks (CNN), such as U-NET [17], FCN [18], QCANet [19], etc. Because of CNN's ability to automatically extract image features, these types of algorithms perform exceptionally well in cell segmentation. Nevertheless, high-performance segmentation algorithms rely on a large number of pixel-level labels that are quite time-consuming and labor-intensive. The training process of the model counts on GPU resources which is costly as well. Hence it is still worth trying to perform unsupervised segmentation of images in order to save the above resources to the greatest extent. In this study, we address the problem of non-contact, non-invasive imaging of muscle stem cell in vitro culture and propose an unsupervised semantic segmentation algorithm to extract the portion of the muscle precursor cell (MPCs) images that need to be measured. We design a set of bright field imaging equipment for long-lasting cell monitoring in situ directly through a petri dish for that neither additional marking nor smearing is required.