Modelling A Random Patch Extraction with A Deep U-Net for the Heart Image Segmentation Process
Keywords:
Statistical shape model, 3D segmentation, Deep learning, Image segmentation, Feature extraction.Abstract
Deep learning is extensively utilized in medical image processing. Moreover, present deep neural network models were completely based on the huge amount of labelled training data; but, medical image-based segmentation tasks generally suffer from labelling smaller quality data as these medical image labelling are extremely costly and time-consuming. To overcome these complexities, this work anticipates a new image patch extraction strategy concerning features of heart images which may produce numerous simulated images from a smaller number of real-time images. Initially, shape information regarding real labelled images is modelled with random patch extraction by sampling the input images. Subsequently, these tumor shapes are labelled using textures of a 3D thin plate to produce simulated images. At last, these simulated and real images are provided for training deep neural networks with a U-net model for segmentation. The anticipated model undergoes segmenting, which can be utilized in any anatomical structure-based segmentation tasks with deep neural network architecture. The Automated Cardiac Diagnosis Challenge (ACDC) dataset has been considered for performing segmentation that includes 3D convolutional neural networks. The experimental outcomes demonstrate that the proposed segmentation strategy may enhance the accuracy of the existing segmentation process based on deep neural networks.
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