论文标题
L-CO-NET：从心脏Cine MRI进行临床参数估计的学习学到的凝结优化网络
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI
论文作者
论文摘要
在这项工作中，我们实施了一个完全卷积的细分器，该分段既有学习的组结构，又具有正规的权重螺旋体，以降低体积图像分段的高计算成本。我们在ACDC数据集上验证了我们的框架，该数据集由一个在整个心脏周期中成像的健康和四个病理组。我们的技术达到了96.8％（LV血液），93.3％（RV血液池）和90.0％（LV心肌）的骰子评分，并具有五倍的交叉验证，并产生了与从地面真实分割数据中估计的相似的临床参数。基于这些结果，该技术有可能成为一种有效且竞争性的心脏图像分割工具，可用于心脏计算机辅助诊断，计划和指导应用。
In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation. We validated our framework on the ACDC dataset featuring one healthy and four pathology groups imaged throughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LV blood-pool), 93.3% (RV blood-pool) and 90.0% (LV Myocardium) with five-fold cross-validation and yielded similar clinical parameters as those estimated from the ground truth segmentation data. Based on these results, this technique has the potential to become an efficient and competitive cardiac image segmentation tool that may be used for cardiac computer-aided diagnosis, planning, and guidance applications.
