Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot

Abstract

Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm’s performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review

“…There were 50 studies that broadly focused on the application of machine learning in paediatric cardiology research. These studies were categorised and focused on various intentions for clinical use (Figure 3): diagnosis and assessment of underlying critical and non-critical CHD (n = 20), 1,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] prediction and risk stratification of outcomes in CHD (n = 15), [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] management of patients with CHD (n = 2), 53,54 medical device research (n = 4), [55][56][57][58] novel genetics and biomarkers in CHD (n = 5), [59][60][61][62][63] CHD in pregnancy (n = 3),…”

“…While cardiac imaging has already been established as a standard and definitive way to diagnose complex heart disease, investigators are now taking advantage of the machine learning applications that can unveil hidden patterns in these data to boost diagnostic accuracy. 31,32,35 Bruse and colleagues 24 used agglomerative hierarchical clustering and principal component analysis to detect clinically meaningful shape clusters using anatomical cardiovascular MRI data. Subjects with and without surgically corrected coarctation of the aorta and subjects with healthy aortic arches were included.…”

“…(Continued ) Tandon A, et al (2021)35 Cohortstudy (n = 87 patients with repaired tetralogy of Fallot and pulmonary stenosis or atresia, age in the training dataset was 13.5 years [IQR 10-17.5], and age in the testing dataset was 13.9 [IQR 11.7-18]) to automate ventricular contouring during CMR of repaired tetralogy of Fallot patients. One clinical site and one scanner type was used.…”

SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition

“…Comparison with State-of-the-Art Approaches. We compare the proposed SOSPCNN model with three other approaches: MC [8], 3DCNN [9], and VCCNN [10]. The results are shown in Table 7.…”

“…Giannakidis et al (2016) [9] presented a multiscale threedimensional CNN (3DCNN) for segmentation of the right ventricle. Tandon et al (2021) [10] present a ventricular contouring CNN (VCCNN) algorithm.…”