Abstract：
BACKGROUND: The volume measurement of intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) provides critical information for precise treatment of patients with spontaneous ICH but remains a big challenge, especially for IVH segmentation. However, the previously proposed ICH and IVH segmentation tools lack external validation and segmentation quality assessment.
OBJECTIVE: This study aimed to develop a robust deep learning model for the segmentation of ICH and IVH with external validation, and to provide quality assessment for IVH segmentation.
METHODS: In this study, a Residual Encoding Unet (REUnet) for the segmentation of ICH and IVH was developed using a dataset composed of 977 CT images (all contained ICH, and 338 contained IVH; a five-fold cross-validation procedure was adopted for training and internal validation), and externally tested using an independent dataset consisting of 375 CT images (all contained ICH, and 105 contained IVH). The performance of REUnet was compared with six other advanced deep learning models. Subsequently, three approaches, including Prototype Segmentation (ProtoSeg), Test Time Dropout (TTD), and Test Time Augmentation (TTA), were employed to derive segmentation quality scores in the absence of ground truth to provide a way to assess the segmentation quality in real practice.
RESULTS: For ICH segmentation, the median (lower-quantile-upper quantile) of Dice scores obtained from REUnet were 0.932 (0.898-0.953) for internal validation and 0.888 (0.859-0.916) for external test, both of which were better than those of other models while comparable to that of nnUnet3D in external test. For IVH segmentation, the Dice scores obtained from REUnet were 0.826 (0.757-0.868) for internal validation and 0.777 (0.693-0.827) for external tests, which were better than those of all other models. The concordance correlation coefficients between the volumes estimated from the REUnet-generated segmentations and those from the manual segmentations for both ICH and IVH ranged from 0.944 to 0.987. For IVH segmentation quality assessment, the segmentation quality score derived from ProtoSeg was correlated with the Dice Score (Spearman r = 0.752 for the external test) and performed better than those from TTD (Spearman r = 0.718) and TTA (Spearman r = 0.260) in the external test. By setting a threshold to the segmentation quality score, we were able to identify low-quality IVH segmentation results by ProtoSeg.
CONCLUSIONS: The proposed REUnet offers a promising tool for accurate and automated segmentation of ICH and IVH, and for effective IVH segmentation quality assessment, and thus exhibits the potential to facilitate therapeutic decision-making for patients with spontaneous ICH in clinical practice.
OBJECTIVE: This study aimed to develop a robust deep learning model for the segmentation of ICH and IVH with external validation, and to provide quality assessment for IVH segmentation.
METHODS: In this study, a Residual Encoding Unet (REUnet) for the segmentation of ICH and IVH was developed using a dataset composed of 977 CT images (all contained ICH, and 338 contained IVH; a five-fold cross-validation procedure was adopted for training and internal validation), and externally tested using an independent dataset consisting of 375 CT images (all contained ICH, and 105 contained IVH). The performance of REUnet was compared with six other advanced deep learning models. Subsequently, three approaches, including Prototype Segmentation (ProtoSeg), Test Time Dropout (TTD), and Test Time Augmentation (TTA), were employed to derive segmentation quality scores in the absence of ground truth to provide a way to assess the segmentation quality in real practice.
RESULTS: For ICH segmentation, the median (lower-quantile-upper quantile) of Dice scores obtained from REUnet were 0.932 (0.898-0.953) for internal validation and 0.888 (0.859-0.916) for external test, both of which were better than those of other models while comparable to that of nnUnet3D in external test. For IVH segmentation, the Dice scores obtained from REUnet were 0.826 (0.757-0.868) for internal validation and 0.777 (0.693-0.827) for external tests, which were better than those of all other models. The concordance correlation coefficients between the volumes estimated from the REUnet-generated segmentations and those from the manual segmentations for both ICH and IVH ranged from 0.944 to 0.987. For IVH segmentation quality assessment, the segmentation quality score derived from ProtoSeg was correlated with the Dice Score (Spearman r = 0.752 for the external test) and performed better than those from TTD (Spearman r = 0.718) and TTA (Spearman r = 0.260) in the external test. By setting a threshold to the segmentation quality score, we were able to identify low-quality IVH segmentation results by ProtoSeg.
CONCLUSIONS: The proposed REUnet offers a promising tool for accurate and automated segmentation of ICH and IVH, and for effective IVH segmentation quality assessment, and thus exhibits the potential to facilitate therapeutic decision-making for patients with spontaneous ICH in clinical practice.
摘要：
背景：脑出血（ICH）和脑室内出血（IVH）的体积测量为自发性ICH患者的精确治疗提供了关键信息，但仍然是一个巨大的挑战，特别是IVH分割。然而,先前提出的ICH和IVH分割工具缺乏外部验证和分割质量评估.
目的：本研究旨在通过外部验证，为ICH和IVH的分割开发一个健壮的深度学习模型，并为IVH分割提供质量评估。
方法：在本研究中，a用于ICH和IVH分割的残差编码Unet（REUnet）是使用由977个CT图像组成的数据集开发的（所有包含ICH，338包含IVH；采用了五重交叉验证程序进行培训和内部验证），并使用由375张CT图像组成的独立数据集进行外部测试（所有包含ICH，105包含IVH)。将REUnet的性能与其他六种高级深度学习模型进行了比较。随后，三种方法，包括原型分割(ProtoSeg)，测试时间丢失(TTD),和测试时间增强(TTA)，用于在没有地面实况的情况下得出分割质量分数，以提供一种在实际实践中评估分割质量的方法。
结果：对于ICH分段，从REUnet获得的Dice评分的中位数（低分位数-高分位数）内部验证为0.932（0.898-0.953），外部测试为0.888（0.859-0.916），两者都优于其他模型，同时在外部测试中与nnUnet3D相当。对于IVH分割，从REUnet获得的骰子分数为内部验证的0.826（0.757-0.868）和外部测试的0.777（0.693-0.827），比所有其他型号都好。从REUnet生成的分割估计的体积与从ICH和IVH的手动分割估计的体积之间的一致相关系数范围为0.944至0.987。对于IVH分割质量评估，来自ProtoSeg的分割质量评分与Dice评分相关(外部测试的Spearmanr=0.752),并且在外部测试中表现优于TTD(Spearmanr=0.718)和TTA(Spearmanr=0.260).通过为分割质量分数设置阈值，我们能够通过ProtoSeg识别低质量的IVH分割结果。
结论：提出的REUnet为准确和自动分割ICH和IVH提供了一个有前途的工具，以及有效的IVH分割质量评估，因此，在临床实践中显示出促进自发性ICH患者治疗决策的潜力。
目的：本研究旨在通过外部验证，为ICH和IVH的分割开发一个健壮的深度学习模型，并为IVH分割提供质量评估。
方法：在本研究中，a用于ICH和IVH分割的残差编码Unet（REUnet）是使用由977个CT图像组成的数据集开发的（所有包含ICH，338包含IVH；采用了五重交叉验证程序进行培训和内部验证），并使用由375张CT图像组成的独立数据集进行外部测试（所有包含ICH，105包含IVH)。将REUnet的性能与其他六种高级深度学习模型进行了比较。随后，三种方法，包括原型分割(ProtoSeg)，测试时间丢失(TTD),和测试时间增强(TTA)，用于在没有地面实况的情况下得出分割质量分数，以提供一种在实际实践中评估分割质量的方法。
结果：对于ICH分段，从REUnet获得的Dice评分的中位数（低分位数-高分位数）内部验证为0.932（0.898-0.953），外部测试为0.888（0.859-0.916），两者都优于其他模型，同时在外部测试中与nnUnet3D相当。对于IVH分割，从REUnet获得的骰子分数为内部验证的0.826（0.757-0.868）和外部测试的0.777（0.693-0.827），比所有其他型号都好。从REUnet生成的分割估计的体积与从ICH和IVH的手动分割估计的体积之间的一致相关系数范围为0.944至0.987。对于IVH分割质量评估，来自ProtoSeg的分割质量评分与Dice评分相关(外部测试的Spearmanr=0.752),并且在外部测试中表现优于TTD(Spearmanr=0.718)和TTA(Spearmanr=0.260).通过为分割质量分数设置阈值，我们能够通过ProtoSeg识别低质量的IVH分割结果。
结论：提出的REUnet为准确和自动分割ICH和IVH提供了一个有前途的工具，以及有效的IVH分割质量评估，因此，在临床实践中显示出促进自发性ICH患者治疗决策的潜力。
