A Novel Weakly Supervised Segmentation Approach for Rapid Left Ventricle Annotation

In the field of medical image segmentation, convolutional neural networks stand out as a successful method. However, in order to perform well, several labeled images are required. Manual pixel-level annotation of medical images requires the presence of a well-trained expert, is time-consuming, and is expensive. Weakly supervised learning approaches aim to address these challenges. In this work, we propose a novel weakly supervised segmentation approach specifically designed for the left ventricle. By utilizing the circular shape of the left ventricle, we introduce a weak annotation framework based on concentric circles, representing pixels inside and outside the ventricle. Our weakly supervised learning approach incorporates a loss function that ignores unannotated pixels and incorporates total variation regularization for smooth predictions. Our method significantly reduces annotation time from several minutes to 5-10 seconds per scan and eliminates the need for expert presence. We validated our method on the Sunnybrook dataset and our method reached around 0.96 of the accuracy of the networks trained in fully supervised manner (with pixel-level annotations). The implementation of our work is available at "https://github.com/behnam-rahmati/LV-weaklysupervised"