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 arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, 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 provides 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 show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets was limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including the 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 (\url{www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/}).
翻译:整个心脏解剖知识是许多临床应用的先决条件。全心解剖(HHS) 描述心脏子结构的全心解剖(WHS) 可能对心脏解剖和功能的建模和分析非常宝贵。 但是,由于心脏形状差异很大,临床数据图像质量也不同,因此,这种解剖的自动化可能很艰巨。要实现这一目标,通常需要一套培训数据来构建前科或培训。此外,很难对不同方法进行比较,这主要是因为所使用的数据集和评价指标的差异。本稿展示了从多心解剖(MMM-HHS)提交材料中选择的WHS算法的方法和评估结果。然而,由于心脏剖析(MMM-HHS)的挑战与MICCAI 2017年的数据质量差异很大,这种分析可能很艰巨。 挑战提供了120张三维心图,包括60个CT和60个MRI书卷,都是通过手工解析的临床环境获得的。 由12个组提交的CT数据和11个对挑战的解算法进行了十次的算法的比较。这个手法展示的结果可能并不准确,尽管通过内部分析,但是数据也显示, 提供了一个基于培训的精度,但数据分析结果,但数据分析结果中的许多是持续进行。,数据是持续进行的,数据,数据,但数据是持续进行的, 正确的, 正确的,数据分析是持续进行中的数据是持续。