Deep-CodecG*: A Generalized Deep Autoencoder for Robust Segmentation of Left Atrium in Cardiac MRIs: Deep-CodecG* for Robust Segmentation of Left Atrium. - Rawat, Akhilesh; Kumar, Rajeev (INFOCOMP Journal of Computer Science, 2024)
The left atrium receives oxygenated blood from pulmonary veins and is a vital organ concerning congestive heart failure. Several deep learning-based architectures and learning methodologies have been proposed for left atrium semantic segmentation. These studies have shown good performance in learning known datasets. However, generalization remains challenging. In this work, we propose a deep auto-encoder architecture with generalization ability which we call Deep-CodecG*. The proposed model utilized a CNN-based auto-encoder in which the standard convolution is replaced with a two-convolution layer block. This proposed model is generalization enabled with a proper parameterization for (near-) optimal performance. The proposed Deep-CodecG* improves performance on unseen test data, a dice score of 0.95, which is 6.3% higher than that of a standard auto-encoder. The proposed model gave higher sensitivity, specificity, Jaccard, and structural similarity values and lower Hausdorff distance indicating improvement over an autoencoder with similar two-convolution layer blocks. Though these quantitative improvements seem marginal, they are shown to have a significant impact. The segmented left atrium images match the ground-truth data very closely. Thus, the proposed Deep-CodecG* architecture for left atrium segmentation exhibits well-generalized and robust performance over various image datasets