| Autor: |
Hasan SMK; Center for Imaging Science, Rochester Institute of Technology, NY, USA., Linte CA; Center for Imaging Science, Rochester Institute of Technology, NY, USA.; Biomedical Engineering, Rochester Institute of Technology, NY, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
Proceedings of SPIE--the International Society for Optical Engineering [Proc SPIE Int Soc Opt Eng] 2020 Feb; Vol. 11315. Date of Electronic Publication: 2020 Mar 16. |
| DOI: |
10.1117/12.2550640 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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