FM-Unet: Biomedical image segmentation based on feedback mechanism Unet

ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation

Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT images is a tedious task, and due to the diversity of these images and varying technical skills of professionals, segmentation results can be inconsistent. To address this problem, a novel ASD-Net network is proposed in this paper for kidney and kidney tumor segmentation tasks. First, the proposed network employs newly designed Adaptive Spatial-channel Convolution Optimization (ASCO) blocks to capture anisotropic information in the images. Then, other newly designed blocks, i.e., Dense Dilated Enhancement Convolution (DDEC) blocks, are utilized to enhance feature propagation and reuse it across the network, thereby improving its segmentation accuracy. To allow the network to segment complex and small kidney tumors more effectively, the Atrous Spatial Pyramid Pooling (ASPP) module is incorporated in its middle layer. With its generalized pyramid feature, this module enables the network to better capture and understand context information at various scales within the images. In addition to this, the concurrent spatial and channel squeeze & excitation (scSE) attention mechanism is adopted to better comprehend and manage context information in the images. Additional encoding layers are also added to the base (U-Net) and connected to the original encoding layer through skip connections. The resultant enhanced U-Net structure allows for better extraction and merging of high-level and low-level features, further boosting the network's ability to restore segmentation details. In addition, the combined Binary Cross Entropy (BCE)-Dice loss is utilized as the network's loss function. Experiments, conducted on the KiTS19 dataset, demonstrate that the proposed ASD-Net network outperforms the existing segmentation networks according to all evaluation metrics used, except for recall in the case of kidney tumor segmentation, where it takes the second place after Attention-UNet.

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%.