PDF(Pubmed)
Abstract：
UNASSIGNED: Tumor delineation is required both for radiotherapy planning and quantitative imaging biomarker purposes. It is a manual, time- and labor-intensive process prone to inter- and intraobserver variations. Semi or fully automatic segmentation could provide better efficiency and consistency. This study aimed to investigate the influence of including and combining functional with anatomical magnetic resonance imaging (MRI) sequences on the quality of automatic segmentations.
UNASSIGNED: T2-weighted (T2w), diffusion weighted, multi-echo T2*-weighted, and contrast enhanced dynamic multi-echo (DME) MR images of eighty-one patients with rectal cancer were used in the analysis. Four classical machine learning algorithms; adaptive boosting (ADA), linear and quadratic discriminant analysis and support vector machines, were trained for automatic segmentation of tumor and normal tissue using different combinations of the MR images as input, followed by semi-automatic morphological post-processing. Manual delineations from two experts served as ground truth. The Sørensen-Dice similarity coefficient (DICE) and mean symmetric surface distance (MSD) were used as performance metric in leave-one-out cross validation.
UNASSIGNED: Using T2w images alone, ADA outperformed the other algorithms, yielding a median per patient DICE of 0.67 and MSD of 3.6 mm. The performance improved when functional images were added and was highest for models based on either T2w and DME images (DICE: 0.72, MSD: 2.7 mm) or all four MRI sequences (DICE: 0.72, MSD: 2.5 mm).
UNASSIGNED: Machine learning models using functional MRI, in particular DME, have the potential to improve automatic segmentation of rectal cancer relative to models using T2w MRI alone.
UNASSIGNED: T2-weighted (T2w), diffusion weighted, multi-echo T2*-weighted, and contrast enhanced dynamic multi-echo (DME) MR images of eighty-one patients with rectal cancer were used in the analysis. Four classical machine learning algorithms; adaptive boosting (ADA), linear and quadratic discriminant analysis and support vector machines, were trained for automatic segmentation of tumor and normal tissue using different combinations of the MR images as input, followed by semi-automatic morphological post-processing. Manual delineations from two experts served as ground truth. The Sørensen-Dice similarity coefficient (DICE) and mean symmetric surface distance (MSD) were used as performance metric in leave-one-out cross validation.
UNASSIGNED: Using T2w images alone, ADA outperformed the other algorithms, yielding a median per patient DICE of 0.67 and MSD of 3.6 mm. The performance improved when functional images were added and was highest for models based on either T2w and DME images (DICE: 0.72, MSD: 2.7 mm) or all four MRI sequences (DICE: 0.72, MSD: 2.5 mm).
UNASSIGNED: Machine learning models using functional MRI, in particular DME, have the potential to improve automatic segmentation of rectal cancer relative to models using T2w MRI alone.
摘要：
未经授权：放疗计划和定量成像生物标志物目的都需要肿瘤勾画。这是一个手册，时间和劳动密集型的过程容易出现观察者之间和观察者之间的变化。半自动或全自动分割可以提供更好的效率和一致性。本研究旨在研究包含和结合功能与解剖磁共振成像（MRI）序列对自动分割质量的影响。
未经评估：T2加权（T2w），扩散加权，多回波T2*加权，分析中使用了81例直肠癌患者的动态多回声（DME）MR图像。四种经典的机器学习算法；自适应增强(ADA)，线性和二次判别分析和支持向量机，使用MR图像的不同组合作为输入来训练肿瘤和正常组织的自动分割，其次是半自动形态学后处理。两位专家的人工描述是事实。Sørensen-Dice相似性系数（DICE）和平均对称表面距离（MSD）用作留一交叉验证中的性能指标。
未经评估：单独使用T2w图像，ADA优于其他算法，每位患者的平均DICE为0.67，MSD为3.6毫米。当添加功能图像时，性能得到改善，对于基于T2w和DME图像（DICE：0.72，MSD：2.7mm）或所有四个MRI序列（DICE：0.72，MSD：2.5mm）的模型，性能最高。
未经评估：使用功能性MRI的机器学习模型，特别是DME,相对于单独使用T2wMRI的模型，有可能改善直肠癌的自动分割。
未经评估：T2加权（T2w），扩散加权，多回波T2*加权，分析中使用了81例直肠癌患者的动态多回声（DME）MR图像。四种经典的机器学习算法；自适应增强(ADA)，线性和二次判别分析和支持向量机，使用MR图像的不同组合作为输入来训练肿瘤和正常组织的自动分割，其次是半自动形态学后处理。两位专家的人工描述是事实。Sørensen-Dice相似性系数（DICE）和平均对称表面距离（MSD）用作留一交叉验证中的性能指标。
未经评估：单独使用T2w图像，ADA优于其他算法，每位患者的平均DICE为0.67，MSD为3.6毫米。当添加功能图像时，性能得到改善，对于基于T2w和DME图像（DICE：0.72，MSD：2.7mm）或所有四个MRI序列（DICE：0.72，MSD：2.5mm）的模型，性能最高。
未经评估：使用功能性MRI的机器学习模型，特别是DME,相对于单独使用T2wMRI的模型，有可能改善直肠癌的自动分割。
