DLGRAFE-Net: A double loss guided residual attention and feature enhancement network for polyp segmentation.

Colon polyps represent a common gastrointestinal form. In order to effectively treat and prevent complications arising from colon polyps, colon polypectomy has become a commonly used therapeutic approach. Accurately segmenting polyps from colonoscopy images can provide valuable information for early diagnosis and treatment. Due to challenges posed by illumination and contrast variations, noise and artifacts, as well as variations in polyp size and blurred boundaries in polyp images, the robustness of segmentation algorithms is a significant concern. To address these issues, this paper proposes a Double Loss Guided Residual Attention and Feature Enhancement Network (DLGRAFE-Net) for polyp segmentation. Firstly, a newly designed Semantic and Spatial Information Aggregation (SSIA) module is used to extract and fuse edge information from low-level feature graphs and semantic information from high-level feature graphs, generating local loss-guided training for the segmentation network. Secondly, newly designed Deep Supervision Feature Fusion (DSFF) modules are utilized to fuse local loss feature graphs with multi-level features from the encoder, addressing the negative impact of background imbalance caused by varying polyp sizes. Finally, Efficient Feature Extraction (EFE) decoding modules are used to extract spatial information at different scales, establishing longer-distance spatial channel dependencies to enhance the overall network performance. Extensive experiments conducted on the CVC-ClinicDB and Kvasir-SEG datasets demonstrate that the proposed network outperforms all mainstream networks and state-of-the-art networks, exhibiting superior performance and stronger generalization capabilities.

ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels.

The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully utilizes the low-level and deep-level features extracted by the ResDAC blocks. Finally, parallel dilated convolutions are employed to increase the receptive field to capture multiscale spatial information. ResDAC-Net is highly compatible to the existing state-of-the-art models, according to three (out of four) evaluation metrics, including the two main ones used for segmentation performance evaluation (i.e., DSC and Jaccard index).

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.