Abstract

Despite advances in deep learning methods aimed at cardiac ventricular segmentation, most algorithms have drawbacks due to low prediction accuracy with images from different MR scans to those trained. It leads to a process that requires time-consuming correction by technicians or specialists. The time in this process is significant mainly due to the large number of image sets to be processed. The lack of description of the algorithms has not allowed repeatability, while commercial software is difficult to access for clinical use or research. However, in cardiac segmentation research, several solutions have already been proposed. This paper presents an opensource cardiac functionality segmentation and evaluation framework, which contemplates a diverse database for network training, a multi domain network architecture that allows model generalization, and pre-and post-processing algorithms that improve prediction results. The prediction evaluation of the framework shows that Ventseg is 3.66% superior to the trained model and the similarity percentages in the tested MR scores are over 84%. On the other hand, the inter-observer variability analysis, with anonymized data, shows in the different metrics that Ventseg is on par with cardiac segmentation specialists. Finally, the efficiency calculated in an intra-observer test indicates that our framework reduces manual segmentation time by approximately 80%.