小柯机器人

德国海德堡大学Klaus H. Maier-Hein课题组开发出一种基于深度学习的生物医学图像分割方法。2020年12月7日，《自然—方法学》杂志在线发表了这项成果。

研究人员开发了nnU-Net，这是一种基于深度学习的细分方法，可以自动进行自我配置，包括针对任何新任务的预处理、网络架构、训练和后处理。此过程中的关键设计选择被建模为一组固定参数、相互依赖的规则和经验性决策。无需人工干预，nnU-Net可以超越大多数现有方法，包括对国际生物医学分割竞赛中使用的23个公共数据集提供高度专业化的解决方案。研究人员将nnU-Net作为一个开箱即用的工具公开提供，通过不需要标准网络培训之外的专业知识或计算资源，即可让广大研究者使用。

据介绍，生物医学成像是科学发现的驱动力，也是医疗保健的核心组成部分，并且受到深度学习领域的影响。尽管语义分割算法可以在许多应用程序中进行图像分析和量化，但是各个专用解决方案的设计并非易事，并且高度依赖于数据集属性和硬件条件。

附：英文原文

Title: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Author: Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein

Issue&Volume: 2020-12-07

Abstract: Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training. nnU-Net is a deep learning-based image segmentation method that automatically configures itself for diverse biological and medical image segmentation tasks. nnU-Net offers state-of-the-art performance as an out-of-the-box tool.

DOI: 10.1038/s41592-020-01008-z

Source: https://www.nature.com/articles/s41592-020-01008-z

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex