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Myocardial Infarction (MI), commonly known as heart attack, is the irreversible death of the Myocardium’s tissue due to oxygen deprivation for an extended period of time. LGE-MRI scans are considered the defacto in the diagnosis and prognosis of MI. Still, they require manually segmenting the Myocardium and the infarcted tissue, which is a complex and time-consuming task. Hence, an automatic segmentation method of the Myocardium tissue is highly desirable. CNNs (Convolutional Neural Networks) are used extensively in cardiac tissue segmentation in general and for solving this problem particularly. EMIDEC (automatic Evaluation of Myocardial Infarction from Delayed Enhancement Cardiac MRI) challenge in MICCAI 2020 provides a good overview of the different CNN-based architectures used for MI segmentation. Still, they required complex pipelines to achieve state-of-the-art results and the research followed the challenge tends to go in the same direction of building more complex pipelines revolving around the famous CNN-UNet either 2D or 3D. In this paper, we present a different direction by presenting a simple 2D novel architecture based on Self-Attention Transformers offering a possible alternative to the CNN-UNet as a building block for bigger systems. We introduce NesT-UNet a novel segmentation architecture based on the NesT architecture as an encoder and we also introduce a novel decoder inspired by the same architecture. NesT achieved state-of-the-art results on ImageNet and CIFAR classification tasks with minimal training compared to other transformer networks. 2D NesT-UNet produced results comparable to the state-of-the-art on the EMIDEC dataset using a simple training process and an extra self-supervised pre-training step to improve the network’s performance. we also present a novel loss function for false positives reduction.
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