论文标题
多器官细分中的深度学习
Deep Learning in Multi-organ Segmentation
论文作者
论文摘要
本文介绍了多器官细分中深度学习（DL）的综述。我们总结了用于医疗图像细分和应用的最新基于DL的方法。这些方法根据其网络设计分为六个类别。对于每个类别，我们列出了调查的作品，强调了重要的贡献并确定了具体的挑战。在对每个类别的详细审查之后，我们简要讨论了其成就，缺点和未来潜力。我们使用基准数据集（包括2017年AAPM胸腔自动分割挑战数据集）和2015 Miccai Head Neck自动细分挑战数据集（2017 AAPM Thoracic自动分割挑战数据集），提供了基于DL的胸腔和头部和颈部多机分割方法的全面比较。
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.
