.png){kind=icon}
返回
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1038/s41592-020-01008-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. 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.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
32.1
论文数:
7.2K
被引数:
12.7W
机构
引用论文
Clinically applicable deep learning for diagnosis and referral in retinal disease深度学习在视网膜疾病诊断和转诊中的临床应用.png)
NATURE MEDICINE
NATURE MEDICINEIF50
PREAI
分享收藏Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach使用定量放射组学方法通过无创成像解码肿瘤表型NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Analysis of the formation of monodisperse populations by homogeneous nucleation通过均相成核分析单分散种群的形成
PREAI
分享收藏U-Net: deep learning for cell counting, detection, and morphometryU-net: 用于细胞计数、检测和形态测量的深度学习NATURE METHODS
NATURE METHODSIF32.1
PREAI
分享收藏MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy有丝分裂原: 在荧光显微镜下生成细胞群的3D合成延时序列的框架
PREAI
分享收藏
查看更多内容