PDF(Pubmed)
Abstract：
UNASSIGNED: Addressing the challenges of unclear tumor boundaries and the confusion between cysts and tumors in liver tumor segmentation, this study aims to develop an auto-segmentation method utilizing Gaussian filter with the nnUNet architecture to effectively distinguish between tumors and cysts, enhancing the accuracy of liver tumor auto-segmentation.
UNASSIGNED: Firstly, 130 cases of liver tumorsegmentation challenge 2017 (LiTS2017) were used for training and validating nnU-Net-based auto-segmentation model. Then, 14 cases of 3D-IRCADb dataset and 25 liver cancer cases retrospectively collected in our hospital were used for testing. The dice similarity coefficient (DSC) was used to evaluate the accuracy of auto-segmentation model by comparing with manual contours.
UNASSIGNED: The nnU-Net achieved an average DSC value of 0.86 for validation set (20 LiTS cases) and 0.82 for public testing set (14 3D-IRCADb cases). For clinical testing set, the standalone nnU-Net model achieved an average DSC value of 0.75, which increased to 0.81 after post-processing with the Gaussian filter (P<0.05), demonstrating its effectiveness in mitigating the influence of liver cysts on liver tumor segmentation.
UNASSIGNED: Experiments show that Gaussian filter is beneficial to improve the accuracy of liver tumor segmentation in clinic.
UNASSIGNED: Firstly, 130 cases of liver tumorsegmentation challenge 2017 (LiTS2017) were used for training and validating nnU-Net-based auto-segmentation model. Then, 14 cases of 3D-IRCADb dataset and 25 liver cancer cases retrospectively collected in our hospital were used for testing. The dice similarity coefficient (DSC) was used to evaluate the accuracy of auto-segmentation model by comparing with manual contours.
UNASSIGNED: The nnU-Net achieved an average DSC value of 0.86 for validation set (20 LiTS cases) and 0.82 for public testing set (14 3D-IRCADb cases). For clinical testing set, the standalone nnU-Net model achieved an average DSC value of 0.75, which increased to 0.81 after post-processing with the Gaussian filter (P<0.05), demonstrating its effectiveness in mitigating the influence of liver cysts on liver tumor segmentation.
UNASSIGNED: Experiments show that Gaussian filter is beneficial to improve the accuracy of liver tumor segmentation in clinic.
摘要：
■在肝脏肿瘤分割中解决肿瘤边界不清和囊肿与肿瘤混淆的挑战，本研究旨在开发一种利用高斯滤波器与nnUNet架构的自动分割方法，以有效区分肿瘤和囊肿，提高肝脏肿瘤自动分割的准确性。
■首先，130例肝脏肿瘤分割挑战2017（LiTS2017）用于训练和验证基于nnU-Net的自动分割模型。然后，采用回顾性收集的14例3D-IRCADb数据集和25例肝癌进行检测。利用骰子相似系数(DSC)与手动等值线进行比较,评价自动分割模型的准确性。
■nnU-Net在验证集（20个LiTS案例）和公共测试集（14个3D-IRCADb案例）的平均DSC值为0.86。对于临床测试装置，独立nnU-Net模型的平均DSC值为0.75，经过高斯滤波器的后处理后增加到0.81（P<0.05），证明其在减轻肝囊肿对肝肿瘤分割的影响的有效性。
■实验表明，高斯滤波器有利于提高临床上肝脏肿瘤分割的准确性。
■首先，130例肝脏肿瘤分割挑战2017（LiTS2017）用于训练和验证基于nnU-Net的自动分割模型。然后，采用回顾性收集的14例3D-IRCADb数据集和25例肝癌进行检测。利用骰子相似系数(DSC)与手动等值线进行比较,评价自动分割模型的准确性。
■nnU-Net在验证集（20个LiTS案例）和公共测试集（14个3D-IRCADb案例）的平均DSC值为0.86。对于临床测试装置，独立nnU-Net模型的平均DSC值为0.75，经过高斯滤波器的后处理后增加到0.81（P<0.05），证明其在减轻肝囊肿对肝肿瘤分割的影响的有效性。
■实验表明，高斯滤波器有利于提高临床上肝脏肿瘤分割的准确性。
