Validation of a deep-learning semantic segmentation approach to fully automate MRI-based left-ventricular deformation analysis in cardiotoxicity

Abstract

Objective: Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to chemotherapy for breast cancer. This study investigated an automated and supervised deep convolutional neural network (DCNN) model for LV chamber quantification before strain analysis in DENSE images.

Methods: The DeepLabV3 +DCNN with three versions of ResNet-50 backbone was designed to conduct chamber quantification on 42 female breast cancer data sets. The convolutional layers in the three ResNet-50 backbones were varied as non-atrous, atrous and modified, atrous with accuracy improvements like using Laplacian of Gaussian filters. Parameters such as LV end-diastolic diameter (LVEDD) and ejection fraction (LVEF) were quantified, and myocardial strains analyzed with the Radial Point Interpolation Method (RPIM). Myocardial classification was validated with the performance metrics of accuracy, Dice, average perpendicular distance (APD) and others. Repeated measures ANOVA and intraclass correlation (ICC) with Cronbach's α (C-Alpha) tests were conducted between the three DCNNs and a vendor tool on chamber quantification and myocardial strain analysis.

Results: Validation results in the same test-set for myocardial classification were accuracy = 97%, Dice = 0.92, APD = 1.2 mm with the modified ResNet-50, and accuracy = 95%, Dice = 0.90, APD = 1.7 mm with the atrous ResNet-50. The ICC results between the modified ResNet-50, atrous ResNet-50 and vendor-tool were C-Alpha = 0.97 for LVEF (55±7%, 54±7%, 54±7%, p = 0.6), and C-Alpha = 0.87 for LVEDD (4.6 ± 0.3 cm, 4.6 ± 0.3 cm, 4.6 ± 0.4 cm, p = 0.7).

Conclusion: Similar performance metrics and equivalent parameters obtained from comparisons between the atrous networks and vendor tool show that segmentation with the modified, atrous DCNN is applicable for automated LV chamber quantification and subsequent strain analysis in cardiotoxicity.

Advances in knowledge: A novel deep-learning technique for segmenting DENSE images was developed and validated for LV chamber quantification and strain analysis in cardiotoxicity detection.

Figure 1.

The DeepLabV3 +DCNN with modified Renet-50 backbone for feature recognition of the LV myocardium in DENSE images, shown with convolution, rates, output strides, ASPP, upsampling, scorer and softmax. The modifications include Laplacian of Gaussian filters at the first convolutional layer (Conv1) and fourth block (B4) and replacing the max-pooling with convolutional addition (Add1). The non-atrous version is without atrous convolution and ASPP. ASPP, atrous spatialpyramid pooling; DCNN, deep convolutional neural network; DENSE, displacement encoding with stimulated echoes.

Figure 2.

Pixel-based confusion matrices and ROCs from segmenting the DENSE LV test-set images (N = 2448, 12 sets) with the DCNN and its three backbones consisting of the (a) modified and atrous ResNet-50-M, (b) default atrous ResNet-50-A in DeepLabV3+, and (c) non-atrous ResNet-50-N. Similar results on the validation-set with the three networks are given in Supplementary Material 1. DCNN, deep convolutionalneural network; DENSE, displacement encoding with stimulated echoes; LV, left-ventricular;ROC, receiver operating characteristic.

Figure 3.

(a) Systolic-period 16-bit DICOM-converted RGB input files of a mid-ventricular LV slice from a validation-set patient and (b) corresponding output labels from the modified, atrous ResNet-50-M DCNN. DCNN, deep convolutionalneural network; LV, left-ventricular.

Figure 4.

(a) Systolic-period 16-bit DICOM-converted RGB input files of a mid-ventricular LV slice from a test-set patient and (b) corresponding output labels from the modified, atrous ResNet-50-M DCNN. DCNN, deep convolutionalneural network; LV, left-ventricular.

Figure 5.

Shown from segmentation with the DCNN and its modified ResNet-50-M backbone are: (a) 16 of 64 activations at the output of the first convolutional layer with LoG filters (Conv1 in Figure 1), (b) the corresponding RGB image and its output label, (c) 16 of 256 inputs to the scorer layer for weighted scoring activations (Scorer in Figure 1), (d) the four class-based output activations of the softmax layer (Softmax in Figure 1). DCNN, deep convolutionalneural network; LoG, Laplacian of Gaussian.

Figure 6.

(a) Reconstructed end-diastolic and end-systolic LV geometries generated for chamber quantification from segmentation of DENSE short-axis slices with the DCNN and its modified ResNet-50-M backbone. Agreements between LVEF estimated with the modified, atrous ResNet-50-M backbone in the DCNN and (b) default, atrous ResNet-50-A and (c) non-atrous ResNet-50-N backbones in the DCNN and (d) Circle CVI42. DCNN, deep convolutionalneural network; DENSE, displacement encoding with stimulated echoes; LV, left-ventricular;LVEF, left-ventricular ejection fraction.