Impact of Image Denoising Techniques on CNN-based Liver Vessel Segmentation using Synthesis Low-dose Contrast Enhanced CT Images

Liver vessel segmentation in contrast enhanced CT (CECT) image is relevant for several clinical applications. However, the liver segmentation on noisy images obtain incorrect liver vessel segmentation which may lead to distortion in the simulation of cooling effect near the vessels during the planning. In this study, we present a framework that consists of three well-known and state-of-the-art denoising techniques, Vesssel enhancing diffusion (VED), RED-CNN, and MAP-NN and using a state-of-the-art Convolution Neural Networks (nn-Unet) to segment the liver vessels from the CECT images. The impact of denoising methods on the vessel segmentation are ablated using with multi-level simulated low-dose CECT of the liver. The experiment is carried on CECT images of the liver from two public and one private datasets. We evaluate the performance of the framework using Dice score and sensitivity criteria. Furthermore, we investigate the efficient of denoising on roughness of the surface of liver vessel segmentation. The results from our experiment suggest that denoising methods can improve the liver vessel segmentation quality in the CECT image with high low-dose noise while they degrade the liver vessel segmentation accuracy for low-noise-level CECT images

Automatic hepatic vessels segmentation using RORPO vessel enhancement filter and 3D V-Net with variant Dice loss function

The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a challenging task because of its small diameter. Hepatic vessels are often captured in images of low contrast and resolution. Our research uses filter enhancement to improve their contrast, which helps with their detection and final segmentation. We have designed a specific fusion of the Ranking Orientation Responses of Path Operators (RORPO) enhancement filter with a raw image, and we have compared it with the fusion of different enhancement filters based on Hessian eigenvectors. Additionally, we have evaluated the 3D U-Net and 3D V-Net neural networks as segmentation architectures, and have selected 3D V-Net as a better segmentation architecture in combination with the vessel enhancement technique. Furthermore, to tackle the pixel imbalance between the liver (background) and vessels (foreground), we have examined several variants of the Dice Loss functions, and have selected the Weighted Dice Loss for its performance. We have used public 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb) dataset, in which we have manually improved upon the annotations of vessels, since the dataset has poor-quality annotations for certain patients. The experiments demonstrate that our method achieves a mean dice score of 76.2%, which outperforms other state-of-the-art techniques.Web of Science131art. no. 54