A learnable Gabor Convolution kernel for vessel segmentation.

Vessel segmentation is significant for characterizing vascular diseases, receiving wide attention of researchers. The common vessel segmentation methods are mainly based on convolutional neural networks (CNNs), which have excellent feature learning capabilities. Owing to inability to predict learning direction, CNNs generate large channels or sufficient depth to obtain sufficient features. It may engender redundant parameters. Drawing on performance ability of Gabor filters in vessel enhancement, we built Gabor convolution kernel and designed its optimization. Unlike traditional filter using and common modulation, its parameters are automatically updated using gradients in the back propagation. Since the structural shape of Gabor convolution kernels is the same as that of regular convolution kernels, it can be integrated into any CNNs architecture. We built Gabor ConvNet using Gabor convolution kernels and tested it using three vessel datasets. It scored 85.06%, 70.52% and 67.11%, respectively, ranking first on three datasets. Results shows that our method outperforms advanced models in vessel segmentation. Ablations also proved that Gabor kernel has better vessel extraction ability than the regular convolution kernel.

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.

(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94-00.02%, 60.42-11.25%, 70.79-09.35% and 63.15-08.35%) and public dataset (99.73-00.12%, 77.02-06.06%, 41.23-08.61% and 52.50-08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.