TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF

TLTNet: A novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation

Extracting global and local feature information is still challenging due to the problems of retinal blood vessel medical images like fuzzy edge features, noise, difficulty in distinguishing between lesion regions and background information, and loss of low-level feature information, which leads to insufficient extraction of feature information. To better solve these problems and fully extract the global and local feature information of the image, we propose a novel transscale cascade layered transformer network for enhanced retinal blood vessel segmentation, which consists of an encoder and a decoder and is connected between the encoder and decoder by a transscale transformer cascade module. Among them, the encoder consists of a local-global transscale transformer module, a multi-head layered transscale adaptive embedding module, and a local context(LCNet) module. The transscale transformer cascade module learns local and global feature information from the first three layers of the encoder, and multi-scale dependent features, fuses the hierarchical feature information from the skip connection block and the channel-token interaction fusion block, respectively, and inputs it to the decoder. The decoder includes a decoding module for the local context network and a transscale position transformer module to input the local and global feature information extracted from the encoder with retained key position information into the decoding module and the position embedding transformer module for recovery and output of the prediction results that are consistent with the input feature information. In addition, we propose an improved cross-entropy loss function based on the difference between the deterministic observation samples and the prediction results with the deviation distance, which is validated on the DRIVE and STARE datasets combined with the proposed network model based on the dual transformer structure in this paper, and the segmentation accuracies are 97.26% and 97.87%, respectively. Compared with other state-of-the-art networks, the results show that the proposed network model has a significant competitive advantage in improving the segmentation performance of retinal blood vessel images.

Heart and great vessels segmentation in congenital heart disease via CNN and conditioned energy function postprocessing

PURPOSE

The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CHD present significant challenges to comprehensive heart segmentation.

METHODS

We proposed a novel two-stage segmentation approach, integrating a Convolutional Neural Network (CNN) with a postprocessing method with conditioned energy function for pulmonary and aorta. The initial stage employs a CNN enhanced by a gated self-attention mechanism for the segmentation of five primary heart structures and two major vessels. Subsequently, the second stage utilizes a conditioned energy function specifically tailored to refine the segmentation of the pulmonary artery and aorta, ensuring vascular continuity.

RESULTS

Our method was evaluated on a public dataset including 110 3D CT volumes, encompassing 16 CHD variants. Compared to prevailing segmentation techniques (U-Net, V-Net, Unetr, dynUnet), our approach demonstrated improvements of 1.02, 1.04, and 1.41% in Dice Coefficient (DSC), Intersection over Union (IOU), and the 95th percentile Hausdorff Distance (HD95), respectively, for heart structure segmentation. For the two great vessels, the enhancements were 1.05, 1.07, and 1.42% in these metrics.

CONCLUSION

The outcomes on the public dataset affirm the efficacy of our proposed segmentation method. Precise segmentation of the entire heart and great vessels can significantly aid in the diagnosis and treatment of CHD, underscoring the clinical relevance of our findings.

CoVi-Net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation

Retinal vessel segmentation plays a crucial role in the diagnosis and treatment of ocular pathologies. Current methods have limitations in feature fusion and face challenges in simultaneously capturing global and local features from fundus images. To address these issues, this study introduces a hybrid network named CoVi-Net, which combines convolutional neural networks and vision transformer. In our proposed model, we have integrated a novel module for local and global feature aggregation (LGFA). This module facilitates remote information interaction while retaining the capability to effectively gather local information. In addition, we introduce a bidirectional weighted feature fusion module (BWF). Recognizing the variations in semantic information across layers, we allocate adjustable weights to different feature layers for adaptive feature fusion. BWF employs a bidirectional fusion strategy to mitigate the decay of effective information. We also incorporate horizontal and vertical connections to enhance feature fusion and utilization across various scales, thereby improving the segmentation of multiscale vessel images. Furthermore, we introduce an adaptive lateral feature fusion (ALFF) module that refines the final vessel segmentation map by enriching it with more semantic information from the network. In the evaluation of our model, we employed three well-established retinal image databases (DRIVE, CHASEDB1, and STARE). Our experimental results demonstrate that CoVi-Net outperforms other state-of-the-art techniques, achieving a global accuracy of 0.9698, 0.9756, and 0.9761 and an area under the curve of 0.9880, 0.9903, and 0.9915 on DRIVE, CHASEDB1, and STARE, respectively. We conducted ablation studies to assess the individual effectiveness of the three modules. In addition, we examined the adaptability of our CoVi-Net model for segmenting lesion images. Our experiments indicate that our proposed model holds promise in aiding the diagnosis of retinal vascular disorders.