Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation
This addresses noise reduction in segmentation for cardiac MR imaging, but it is incremental as it applies a filter to existing networks.
The paper tackled noise in convolutional features for 2D cardiac MR image segmentation by proposing a Convolutional Feature Filter (CFF), which reduced noise as measured by a binarization equation for information entropy.
Noise reduction constitutes a crucial operation within Digital Signal Processing. Regrettably, it frequently remains neglected when dealing with the processing of convolutional features in segmentation networks. This oversight could trigger the butterfly effect, impairing the subsequent outcomes within the entire feature system. To complete this void, we consider convolutional features following Gaussian distributions as feature signal matrices and then present a simple and effective feature filter in this study. The proposed filter is fundamentally a low-amplitude pass filter primarily aimed at minimizing noise in feature signal inputs and is named Convolutional Feature Filter (CFF). We conducted experiments on two established 2D segmentation networks and two public cardiac MR image datasets to validate the effectiveness of the CFF, and the experimental findings demonstrated a decrease in noise within the feature signal matrices. To enable a numerical observation and analysis of this reduction, we developed a binarization equation to calculate the information entropy of feature signals.