With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

中文翻译：

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PyConvU-Net：用于生物医学图像分割的轻量级多尺度网络

随着深度学习（DL）的发展，提出了越来越多的基于深度学习的方法，并在生物医学图像分割中实现了最新的性能。但是，这些方法通常很复杂，并且需要强大的计算资源的支持。根据实际情况，在临床情况下使用大量的计算资源是不切实际的。因此，开发依赖于资源约束计算的基于DL的精确生物医学图像分割方法具有重要意义。提出了一种称为PyConvU-Net的轻量级多尺度网络，可用于低资源计算。通过严格控制的实验，PyConvU-Net预测在参数最少的三个生物医学图像分割任务上表现良好。