Calibration of cine MRI segmentation probability for uncertainty estimation using a multi-task cross-task learning architecture.

While deep learning has shown potential in solving a variety of medical image analysis problems including segmentation, registration, motion estimation, etc., their applications in the real-world clinical setting are still not affluent due to the lack of reliability caused by the failures of deep learning models in prediction. Furthermore, deep learning models need a large number of labeled datasets. In this work, we propose a novel method that incorporates uncertainty estimation to detect failures in the segmentation masks generated by CNNs. Our study further showcases the potential of our model to evaluate the correlation between the uncertainty and the segmentation errors for a given model. Furthermore, we introduce a multi-task cross-task learning consistency approach to enforce the correlation between the pixel-level (segmentation) and the geometric-level (distance map) tasks. Our extensive experimentation with varied quantities of labeled data in the training sets justifies the effectiveness of our model for the segmentation and uncertainty estimation of the left ventricle (LV), right ventricle (RV), and myocardium (Myo) at end-diastole (ED) and end-systole (ES) phases from cine MRI images available through the MICCAI 2017 ACDC Challenge Dataset. Our study serves as a proof-of-concept of how uncertainty measure correlates with the erroneous segmentation generated by different deep learning models, further showcasing the potential of our model to flag low-quality segmentation from a given model in our future study.