论文标题
部分可观测时空混沌系统的无模型预测
CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation
论文作者
论文摘要
随着心脏Cine磁共振（CMR）成像的出现，医疗技术的范式转移，这要归功于其在心脏内部不具有电离辐射的情况下对不同结构进行成像的能力。但是，在不准确的分割和鉴定左心室（LV），右心室（RV）血液池和LV肌电心脏的情况下，进行微创心脏手术的术前计划非常具有挑战性。这些结构的手动分割，尽管如此，这些结构很耗时，而且通常容易出现错误和偏见的结果。因此，自动和计算有效的分割技术至关重要。在这项工作中，我们提出了一种新型的记忆效率卷积神经网络（CNN）体系结构，作为Condensenet的修饰，以及通过引入瓶颈块和升压路径来进行心室血液池分割。我们的实验表明，所提出的体系结构在自动化心脏诊断挑战（ACDC）数据集上使用一半（50％），同时仍保持心脏分割的出色精度，同时仍保持了U-NET的记忆需求的一半（50％）。我们在ACDC数据集上验证了一个健康和四个病理组的框架，其心脏图像是在整个心脏周期中获得的，并达到了96.78％（LV血液池），93.46％（RV血液池）和90.1％（LV-Mycardium）的平均骰子得分。这些结果是有希望的，并促进了所提出的方法作为心脏图像分割和临床参数估计的竞争工具，该工具有可能根据需要提供快速准确的结果，并根据需要进行术前计划和/或术前应用。
With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation. However, it is very challenging to conduct pre-operative planning of minimally invasive cardiac procedures without accurate segmentation and identification of the left ventricle (LV), right ventricle (RV) blood-pool, and LV-myocardium. Manual segmentation of those structures, nevertheless, is time-consuming and often prone to error and biased outcomes. Hence, automatic and computationally efficient segmentation techniques are paramount. In this work, we propose a novel memory-efficient Convolutional Neural Network (CNN) architecture as a modification of both CondenseNet, as well as DenseNet for ventricular blood-pool segmentation by introducing a bottleneck block and an upsampling path. Our experiments show that the proposed architecture runs on the Automated Cardiac Diagnosis Challenge (ACDC) dataset using half (50%) the memory requirement of DenseNet and one-twelfth (~ 8%) of the memory requirements of U-Net, while still maintaining excellent accuracy of cardiac segmentation. We validated the framework on the ACDC dataset featuring one healthy and four pathology groups whose heart images were acquired throughout the cardiac cycle and achieved the mean dice scores of 96.78% (LV blood-pool), 93.46% (RV blood-pool) and 90.1% (LV-Myocardium). These results are promising and promote the proposed methods as a competitive tool for cardiac image segmentation and clinical parameter estimation that has the potential to provide fast and accurate results, as needed for pre-procedural planning and/or pre-operative applications.
