1333603 - Improved Robustness for Deep Learning-based Segmentation of Perfusion CMR Datasets Using Data Adaptive Uncertainty-guided Spatiotemporal Analysis

Fully automatic analysis of first-pass perfusion (FPP) myocardial MR datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease [1-3]. With deep learning-based approaches, training well-generalizable deep neural network (DNN) models that, despite having a limited training dataset, are robust across different sites and data-acquisition protocols is an ongoing challenge. Previous work has shown that a “sliding patch” approach for analysis of FPP images generates a data-driven pixelwise “uncertainty map” as a byproduct of the segmentation process [4,5].

Methods:

We propose to leverage the data-driven uncertainty map (dubbed U-map herein) among a pool of multiple trained DNNs (all with the same 3D U-Net architecture but trained with different parameter initializations) to perform Data Adaptive Uncertainty-Guided Spatiotemporal (DAUGS) analysis as in Fig 1, and automatically select the “best” segmentation result with the highest level of certainty (Step 4 in Fig 1). FPP data from 106 patients with suspected ischemia and 14 healthy subjects acquired at 3T from two sites were used: (1) an internal dataset acquired using a saturation recovery (SR) prepared FLASH sequence, and (2) an external dataset acquired with SR-prepared SSFP (Fig 2). Training of DNNs used the data from a subset of the internal dataset (330 stress/rest FPP image series; 90% females). Performance of the proposed DAUGS vs. standard DNN-based analysis was evaluated on a small subset of the internal dataset (no overlap with the training data) and the entire external dataset (120 stress FPP image series; 25% females).

Results:

Fig 3A summarizes the Dice-score comparison of the proposed DAUGS analysis approach vs. standard DNN-based analysis. For the internal dataset, our proposed method and standard approach showed a comparable performance (p >0.5).  However, on the external dataset, ours significantly outperformed the standard approach (Dice: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.01). Fig 3B shows a challenging example (especially the mid slice) from the external test set. The segmentation chosen by the proposed approach performs well with a mean Dice score of >0.90, whereas the standard approach fails. Overall, the number of “failed” segmentations (discontiguous contours) was markedly lower for the proposed method (< 1% vs. 5%).

Conclusion:

Our proposed data-adaptive approach for analysis of FPP datasets offers the flexibility to choose the final segmentation result from a pool of candidate solutions based on the uncertainty level detected by the trained spatiotemporal DNNs. Our results demonstrate that the proposed DAUGS analysis approach improves the generalization ability of DNN-based analysis despite the limited training data, which in turn has the potential to enable automatic analysis of perfusion CMR datasets with improved robustness to variations in the data acquisition protocol (SR-FLASH vs. SR-SSFP), sequence parameters, or site location.