Hasan S M Kamrul, Linte Cristian A
Center for Imaging Science, Rochester Institute of Technology, NY, USA.
Biomedical Engineering, Rochester Institute of Technology, NY, USA.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11315. doi: 10.1117/12.2550640. Epub 2020 Mar 16.
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.
随着心脏电影磁共振（CMR）成像技术的出现，医学技术发生了范式转变，这得益于其能够在不使用电离辐射的情况下对心脏内的不同结构进行成像。然而，在没有对左心室（LV）、右心室（RV）血池和LV心肌进行准确分割和识别的情况下，进行微创心脏手术的术前规划极具挑战性。然而，手动分割这些结构既耗时，又往往容易出错且结果有偏差。因此，自动且计算高效的分割技术至关重要。在这项工作中，我们提出了一种新颖的内存高效卷积神经网络（CNN）架构，通过引入瓶颈块和上采样路径，对CondenseNet以及DenseNet进行修改，用于心室血池分割。我们的实验表明，所提出的架构在自动心脏诊断挑战赛（ACDC）数据集上运行时，内存需求仅为DenseNet的一半（50%），是U-Net内存需求的十二分之一（约8%），同时仍保持出色的心脏分割精度。我们在ACDC数据集上验证了该框架，该数据集包含一个健康组和四个病理组，其心脏图像是在整个心动周期采集的，左心室血池的平均骰子分数为96.78%，右心室血池为93.46%，左心室心肌为90.1%。这些结果很有前景，并将所提出的方法推广为一种用于心脏图像分割和临床参数估计的有竞争力的工具，它有可能根据术前规划和/或术前应用的需要提供快速准确的结果。
