DNL-Net: deformed non-local neural network for blood vessel segmentation

MCNMF-Unet: a mixture Conv-MLP network with multi-scale features fusion Unet for medical image segmentation

Recently, the medical image segmentation scheme combining Vision Transformer (ViT) and multilayer perceptron (MLP) has been widely used. However, one of its disadvantages is that the feature fusion ability of different levels is weak and lacks flexible localization information. To reduce the semantic gap between the encoding and decoding stages, we propose a mixture conv-MLP network with multi-scale features fusion Unet (MCNMF-Unet) for medical image segmentation. MCNMF-Unet is a U-shaped network based on convolution and MLP, which not only inherits the advantages of convolutional in extracting underlying features and visual structures, but also utilizes MLP to fuse local and global information of each layer of the network. MCNMF-Unet performs multi-layer fusion and multi-scale feature map skip connections in each network stage so that all the feature information can be fully utilized and the gradient disappearance problem can be alleviated. Additionally, MCNMF-Unet incorporates a multi-axis and multi-windows MLP module. This module is fully end-to-end and eliminates the need to consider the negative impact of image cropping. It not only fuses information from multiple dimensions and receptive fields but also reduces the number of parameters and computational complexity. We evaluated the proposed model on BUSI, ISIC2018 and CVC-ClinicDB datasets. The experimental results show that the performance of our proposed model is superior to most existing networks, with an IoU of 84.04% and a F1-score of 91.18%.

A Lightweight Network for Contextual and Morphological Awareness for Hepatic Vein Segmentation

Accurate segmentation of the hepatic vein can improve the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. Compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient with few parameters on the two datasets. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.

SDA-UNet: a hepatic vein segmentation network based on the spatial distribution and density awareness of blood vessels

Objective.Hepatic vein segmentation is a fundamental task for liver diagnosis and surgical navigation planning. Unlike other organs, the liver is the only organ with two sets of venous systems. Meanwhile, the segmentation target distribution in the hepatic vein scene is extremely unbalanced. The hepatic veins occupy a small area in abdominal CT slices. The morphology of each person's hepatic vein is different, which also makes segmentation difficult. The purpose of this study is to develop an automated hepatic vein segmentation model that guides clinical diagnosis.Approach.We introduce the 3D spatial distribution and density awareness (SDA) of hepatic veins and propose an automatic segmentation network based on 3D U-Net which includes a multi-axial squeeze and excitation module (MASE) and a distribution correction module (DCM). The MASE restrict the activation area to the area with hepatic veins. The DCM improves the awareness of the sparse spatial distribution of the hepatic veins. To obtain global axial information and spatial information at the same time, we study the effect of different training strategies on hepatic vein segmentation. Our method was evaluated by a public dataset and a private dataset. The Dice coefficient achieves 71.37% and 69.58%, improving 3.60% and 3.30% compared to the other SOTA models, respectively. Furthermore, metrics based on distance and volume also show the superiority of our method.Significance.The proposed method greatly reduced false positive areas and improved the segmentation performance of the hepatic vein in CT images. It will assist doctors in making accurate diagnoses and surgical navigation planning.

SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel Segmentation

A U-Net-based network has achieved competitive performance in retinal vessel segmentation. Previous work has focused on using multilevel high-level features to improve segmentation accuracy but has ignored the importance of shallow-level features. In addition, multiple upsampling and convolution operations may destroy the semantic feature information contained in the decoder layer. To address these problems, we propose a scale and feature aggregate network (SFA-Net), which can make full use of multiscale high-level feature information and shallow features. In this paper, a residual atrous spatial feature aggregate block (RASF) is embedded at the end of the encoder to learn multiscale information. Furthermore, an attentional feature module (AFF) is proposed to enhance the effective fusion between shallow and high-level features. In addition, we designed the multi-path feature fusion (MPF) block to fuse high-level features of different decoder layers, which aims to learn the relationship between the high-level features of different paths and alleviate the information loss. We apply the network to the three benchmark datasets (DRIVE, STARE, and CHASE_DB1) and compare them with the other current state-of-the-art methods. The experimental results demonstrated that the proposed SFA-Net performs effectively, indicating that the network is suitable for processing some complex medical images.