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：近十年来，计算机视觉领域的深度学习研究发展迅速，基于深度学习的生物医学图像分析方法取得了诸多突破。本文介绍MMV_Im2Im——一个用于生物成像应用中图像到图像变换的新型开源Python软件包。MMV_Im2Im基于通用图像到图像变换框架设计，可广泛应用于语义分割、实例分割、图像恢复和图像生成等任务。我们的实现充分利用了最先进的机器学习工程技术，使研究者能够专注于研究本身而无需顾虑工程细节。我们在十余个不同的生物医学问题上验证了MMV_Im2Im的有效性，展示了其通用潜力与适用性。对于计算生物医学研究者而言，MMV_Im2Im可作为开发新型生物医学图像分析或机器学习算法的起点——他们既可复用本包中的代码，也可通过分支扩展本包以促进新方法的开发。实验生物医学研究者则可通过多样化的示例与应用场景，全面理解图像到图像变换的概念。我们希望本工作能为业界提供启示，说明如何将基于深度学习的图像到图像变换整合到分析方法开发流程中，从而开展仅凭传统实验方法无法完成的生物医学研究。为帮助研究者快速上手，我们已在MIT许可证下通过https://github.com/MMV-Lab/mmv_im2im提供MMV_Im2Im的源代码、文档及教程。