Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source python package for image-to-image transformation in bioimaging applications. MMV_Im2Im is designed with a generic image-to-image transformation framework that can be used for a wide range of tasks, including semantic segmentation, instance segmentation, image restoration, and image generation, etc.. Our implementation takes advantage of state-of-the-art machine learning engineering techniques, allowing researchers to focus on their research without worrying about engineering details. We demonstrate the effectiveness of MMV_Im2Im on more than ten different biomedical problems, showcasing its general potentials and applicabilities. For computational biomedical researchers, MMV_Im2Im provides a starting point for developing new biomedical image analysis or machine learning algorithms, where they can either reuse the code in this package or fork and extend this package to facilitate the development of new methods. Experimental biomedical researchers can benefit from this work by gaining a comprehensive view of the image-to-image transformation concept through diversified examples and use cases. We hope this work can give the community inspirations on how DL-based image-to-image transformation can be integrated into the assay development process, enabling new biomedical studies that cannot be done only with traditional experimental assays. To help researchers get started, we have provided source code, documentation, and tutorials for MMV_Im2Im at https://github.com/MMV-Lab/mmv_im2im under MIT license.
翻译:过去十年间,计算机视觉深度学习在生物医学问题中的应用不断发展,深度学习(DL)方法的优化和改进也在不断取得重要进展。本文介绍了一个新的针对生物成像领域的开放源代码 Python 工具包——MMV_Im2Im,它具有一个通用的图像转换框架,可应用于广泛的任务,如语义分割、实例分割、图像恢复和图像生成等应用。我们的实现利用了最先进的机器学习技术,使得研究人员可以专注于研究而无需关注工程细节。我们在十多个不同的生物医学问题上展示了 MMV_Im2Im 的有效性,展示了其普适潜力和适用性。对于计算生物医学研究人员来说,MMV_Im2Im 提供了一个开端,用于开发新的生物医学图像分析或机器学习算法,他们既可以重用本工具包中的代码,也可以 fork 并扩展本工具包以促进新方法的开发。实验生物医学研究人员可以通过各种示例和用例来获得对图像到图像转换概念的全面了解,从而受益于此工作。我们希望此工作可以激发社区如何将基于 DL 的图像到图像转换集成到测定开发过程中,从而实现无法仅使用传统实验测定方法完成的新生物医学研究。为了帮助研究人员快速上手,我们为 MMV_Im2Im 提供了源代码、文档和教程,均可在 https://github.com/MMV-Lab/mmv_im2im(MIT 许可证)上获得。