Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
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DOI：
10.1016/j.media.2019.101537
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发表时间：
2019-12-01
影响因子：
10.9
通讯作者：
Yang, Guang
中科院分区：
文献类型：
--
作者：
Zhuang, Xiahai;Li, Lei;Yang, Guang
了解整个心脏解剖结构是许多临床应用的先决条件。全心脏分割（WHS）描绘了心脏的子结构，对于心脏的解剖和功能的建模和分析非常有价值。然而，由于心脏形状的大变化和临床数据的不同图像质量，自动化这种分割可能具有挑战性。为了实现这一目标，通常需要一组初始训练数据来构建先验或进行训练。此外，很难在不同方法之间进行比较，这主要是由于所使用的数据集和评估指标的差异。本文介绍了从多模态全心脏分割（MM-WHS）挑战的提交材料中选择的WHS算法的方法和评估结果，以及MICCAI 2017。该挑战提供了120个覆盖整个心脏的三维心脏图像，包括60个CT和60个MRI体积，所有这些图像都是在临床环境中通过手动描绘获得的。10算法的CT数据和11算法的MRI数据，提交了12个组，进行了评价。结果表明，CT WHS的性能普遍优于MRI WHS。由于成像的差异和心脏形状的变化，不同类别患者的子结构的分割可能存在不同程度的挑战。基于深度学习（DL）的方法显示出巨大的潜力，尽管其中一些方法在盲法评估中的结果不佳。在不同的网络结构和培训策略中，他们的表现可能会有很大差异。传统的基于多图谱分割的图像分割算法虽然精度和计算效率有限，但表现出良好的性能。挑战，包括提供注释的训练数据和对测试数据提交算法的盲态评估，继续通过其主页（www.sdspeopleludan.edu.cnizhuangxiahai/0/mmwhs/）作为持续的基准资源。(C)2019作者由爱思唯尔公司出版
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeopleludan.edu.cnizhuangxiahai/0/mmwhs/). (C) 2019 The Authors. Published by Elsevier B.V.