Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks

Abstract

Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.

Enhanced Drug Delivery for Cardiac Microvascular Obstruction with an Occlusion-Infusion-Catheter

“… 4 , 6 , 17 The gold standard for MVO diagnosis is non-perfusion within regions of Late-Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging (LGE-CMRI). 3 MRI however is impractical in acute diagnosis and treatment of STEMI patients.…”

Effect of Collateral Flow on Catheter-Based Assessment of Cardiac Microvascular Obstruction

“…OIS was repeated and the corresponding flow-pressure response was used as baseline data for MVO. The presence of MVO was then confirmed by gold standard quantification with LGE-CMRI 3 which was performed within 6 hours after reperfusion.…”

“…Late Gadolinium Enhanced Cardiac Magnetic Resonance Imaging (LGE-CMRI) 3 is the gold-standard technique for MVO quantification. However, MRI is not practical in the acute setting, and therefore, methods which allow for both qualitative and quantitative MVO diagnosis in the catheter lab immediately following treatment of the primary lesion would be very useful.…”

An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net)

“…The validation results proved that the suggested architecture has a comparable performance with a human expert’s delineation (pixel-level accuracy: 95.03%, Kappa statistic: 0.91, Dice: 89.87%, and Hausdorff distance: 5.91 mm). De La Rosa et al [ 12 ] proposed a deep learning-based method for the automatic segmentation and quantification of the scar and MVO tissues in LGE-MRI. Their approach is based on a cascade framework where, firstly, healthy and diseased slices are distinguished by a convolutional neural network.…”