Segmenting the Left Atrium in Cardic CT Images using Deep Learning

Abstract: Convolution neural networks have achieved a state of the art accuracy for multi-class segmentation in biomedical image science. In this thesis, a 2-Stage binary 2D UNet and MultiResUNet are used to segment the 3D cardiac CT Volumes. 3D volumes have been sliced into 2D images. The 2D networks learned to classify the pixels by transforming the information about the segmentation into latent feature space in a contracting path and upsampling them to semantic segmentation in an expanding path. The network trained on diastole and systole timestamp volumes will be able to handle much more extreme morphological differences between the subjects. Evaluation of the results is based on the Dice coefficient as a segmentation metric. The thesis work also explores the impact of the various loss function in image segmentation for the imbalanced dataset. Results show that2-Stage binary UNet has higher performance than MultiResUnet considering segmentation done in all planes. In this work, Convolution neural network prediction uncertainty is estimated using Monte Carlo dropout estimation and it shows that 2-Stage Binary UNet has lower prediction uncertainty than MultiResUNet.