Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning
Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning
复制标题
使用深度学习对左心室和右心室进行心脏 MRI 图像分割
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
I. Altintas
中科院分区：
文献类型：
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作者：
Bo;Daniel Mariano;John Beckfield;Vinay Madenur;Yuming Hu;Tony Reina;Marcus Bobar;M. H. Nguyen;I. Altintas
该项目的目标是使用磁共振成像（MRI）数据为左心室和右心室（LV和RV）分割提供端到端的分析管道。该项目的另一个目的是找到一个模型，将在医学成像数据集的推广。我们利用各种模型、数据集和测试来确定哪一个非常适合这一目的。具体来说，我们实现了三个模型（2-D U-Net，3-D U-Net和DenseNet），并在四个数据集（自动心脏诊断挑战，MICCAI 2009 LV，Sunnybrook Cardiac Data，MICCAI 2012 RV）上对其进行了评估。在保持一致的预处理策略的同时，我们测试了每个模型在与测试数据相同的数据集上训练时的性能，以及在与测试数据集不同的数据集上训练时的性能。数据扩充也被用来提高模型的适应性。结果进行了比较，以确定性能和概括性。
The goal of this project is to use magnetic resonance imaging (MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV and RV) segmentation. Another aim of the project is to find a model that would be generalizable across medical imaging datasets. We utilized a variety of models, datasets, and tests to determine which one is well suited to this purpose. Specifically, we implemented three models (2-D U-Net, 3-D U-Net, and DenseNet), and evaluated them on four datasets (Automated Cardiac Diagnosis Challenge, MICCAI 2009 LV, Sunnybrook Cardiac Data, MICCAI 2012 RV). While maintaining a consistent preprocessing strategy, we tested the performance of each model when trained on data from the same dataset as the test data, and when trained on data from a different dataset than the test dataset. Data augmentation was also used to increase the adaptability of the models. The results were compared to determine performance and generalizability.