A Dense Cascaded Network Model for Outlier Prediction and Segmentation of Cardiac Images
Keywords:
an outlier, cardiac image, segmentation, image reconstruction, transformsAbstract
Deep learning techniques have been effectively used for various applications to segment anatomical features in medical imaging. However, the imagesare split significantly and impact the outcome. Many clinical images with outliers caused by organ motion, patient movement, and image acquisition-related difficulties are frequently ignored topics in the medical image analysis community. This study compares various techniques for segmenting the heart cavity and compensating for outliers. We discuss how image motion outliers affect the segmentation of cardiac MR images. The method's foundation is a freshly developed integrated outlier detection and reconstruction method. Using a joint loss function and the enforcement of a data consistency term successfully transforms the outlier repair job into an under-sampled image reconstruction problem. This study proposes an end-to-end cascaded ResNet architecture with a segmentation network. The first two tasks improved by our instruction are image segmentation, outlier correction, and outlier identification. Using purposefully distorted and uncorrected cardiac images, the network reconstruction is done to fix motion-related aberrations automatically. We demonstrate that excellent segmentation accuracy and respectable image quality are attainable where MRI acquisitions are used as a test set. It was determined whether the fake motion outliers were present. In comparison to several image-correcting structures, this work demonstrates improved performance.
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