  Recently, deep networks have shown impressive performance for the
segmentation of cardiac Magnetic Resonance Imaging (MRI) images. However, their
achievement is proving slow to transition to widespread use in medical clinics
because of robustness issues leading to low trust of clinicians to their
results. Predicting run-time quality of segmentation masks can be useful to
warn clinicians against poor results. Despite its importance, there are few
studies on this problem. To address this gap, we propose a quality control
method based on the agreement across decoders of a multi-view network, TMS-Net,
measured by the cosine similarity. The network takes three view inputs resliced
from the same 3D image along different axes. Different from previous multi-view
networks, TMS-Net has a single encoder and three decoders, leading to better
noise robustness, segmentation performance and run-time quality estimation in
our experiments on the segmentation of the left atrium on STACOM 2013 and
STACOM 2018 challenge datasets. We also present a way to generate poor
segmentation masks by using noisy images generated with engineered noise and
Rician noise to simulate undertraining, high anisotropy and poor imaging
settings problems. Our run-time quality estimation method show a good
classification of poor and good quality segmentation masks with an AUC reaching
to 0.97 on STACOM 2018. We believe that TMS-Net and our run-time quality
estimation method has a high potential to increase the thrust of clinicians to
automatic image analysis tools.
