ResX: Feature Extraction Block for Medical Image Segmentation

Accurate medical image segmentation is critical for various clinical applications, and convolutional neural networks (CNNs) have demonstrated promising results in this field. The performance of CNN models for segmenting specific organs or lesion areas from medical images heavily depends on the feature extraction ability of the backbone network. In this study, we aim to explore the deep features of the backbone network and propose a novel network that can accurately capture multi-scale image features for medical image segmentation. To achieve this goal, we built upon the widely used U-Net framework and evaluated the feature extraction performance of different backbone networks for medical images. Then, we introduced a novel backbone network called ResX block, which utilizes rectangular and dilated convolutions to capture multi-scale features.To validate our conclusions, we conducted experiments on four benchmark datasets, including Lits2017, 3Dircadb, LIDC, and LCTSC. Our results demonstrate that the proposed ResX block outperforms mainstream feature extraction blocks in terms of accuracy and robustness. Our study confirms the importance of accurate multi-scale feature extraction for improving the performance of CNNs in medical image segmentation. Furthermore, we have verified the potential of rectangular and dilated convolutions for capturing multi-scale features in medical images. Finally, we proposed a novel backbone network, the ResX block, which can be seamlessly integrated into any CNN used for medical image segmentation. Our study provides valuable insights for developing more accurate and efficient CNN models for medical image analysis.