Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images

Abstract

Object: The aim of this paper is to investigate the use of fully-convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images. Methods: A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemicheart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation. Results: Protocol 2 outperformed Protocol 1 significantly (p-value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% (inter-quartile range (IQR) = 18.84%) and 71.25% (IQR = 31.82%), respectively. Discussion: Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the automatic segmentation of nonviable scar tissue from CMR-LGE images.

Reference ranges (“normal values”) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update

“…Most published work report methods for automated LV and RV segmentation in Cine CMR [ 3 , 197 – 203 ]. CNN based methods have also been presented for automated quantification of atrial dimensions [ 2 , 204 ], myocardial scar tissue from LGE [ 205 , 206 ], T1 mapping [ 207 ], aortic flow [ 208 ] and disease classification [ 209 ]. Given the potential importance of this topic to the field of CMR, this section summarizes relevant literature in this area and provides a summary of the publicly available CMR data sets relevant to AI segmentation of CMR data.…”

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

“…As future work we would like to investigate deep learning-based strategies, which are today the state of the art in the field of medical-image analysis [ 60 , 61 , 62 ]. Hence, preliminary researches on deep learning algorithms for ECG analysis are providing promising results, but further investigation is needed in the LDV field [ 63 , 64 , 65 ].…”

Compare the Segmentation Accuracy of Heart Substructure on Contrast Enhanced CT by Deep Convolutional Neural Network with Different Loss Functions

“…Precise treatment delivery relies heavily on accurate OARs delineation (38). At present, there have been many researches on automatic heart segmentation, but most of them are carried out on high-quality and well-displayed images such as MRI and CTA (39)(40)(41)(42).This research is carried out on radiotherapy positioning CT, which is the basic image of clinical radiotherapy. The image quality is relatively low and it is difficult to identify the border of the organ.We combined different loss functions into different deep neural networks, and compared their segmentation effects.Tran (43) et al used U-Net-based CNN to segment the right ventricle on cardiac MRI, and obtained an average DSC of 0.9 (95%HD: 5.1mm).…”