SSCFormer: Revisiting ConvNet-Transformer Hybrid Framework from Scale-Wise and Spatial-Channel-Aware Perspectives for Volumetric Medical Image Segmentation.

Accurate and robust medical image segmentation is crucial for assisting disease diagnosis, making treatment plan, and monitoring disease progression. Adaptive to different scale variations and regions of interest is essential for high accuracy in automatic segmentation methods. Existing methods based on the U-shaped architecture respectively tackling intra- and inter-scale problem with a hierarchical encoder, however, are restricted by the scope of multi-scale modeling. In addition, global attention and scaling attention in regions of interest have not been appropriately adopted, especially for the salient features. To address these two issues, we propose a ConvNet-Transformer hybrid framework named SSCFormer for accurate and versatile medical image segmentation. The intra-scale ResInception and inter-scale transformer bridge are designed to collaboratively capture the intra- and inter-scale features, facilitating the interaction of small-scale disparity information at a single stage with large-scale from multiple stages. Global attention and scaling attention are cleverly integrated from a spatial-channel-aware perspective. The proposed SSCFormer is tested on four different medical image segmentation tasks. Comprehensive experimental results show that SSCFormer outperforms the current state-of-the-art methods.

Collaborative networks of transformers and convolutional neural networks are powerful and versatile learners for accurate 3D medical image segmentation.

Integrating transformers and convolutional neural networks represents a crucial and cutting-edge approach for tackling medical image segmentation problems. Nonetheless, the existing hybrid methods fail to fully leverage the strengths of both operators. During the Patch Embedding, the patch projection method ignores the two-dimensional structure and local spatial information within each patch, while the fixed patch size cannot capture features with rich representation effectively. Moreover, the calculation of self-attention results in attention diffusion, hindering the provision of precise details to the decoder while maintaining feature consistency. Lastly, none of the existing methods establish an efficient multi-scale modeling concept. To address these issues, we design the Collaborative Networks of Transformers and Convolutional neural networks (TC-CoNet), which is generally used for accurate 3D medical image segmentation. First, we elaborately design precise patch embedding to generate 3D features with accurate spatial position information, laying a solid foundation for subsequent learning. The encoder-decoder backbone network is then constructed by TC-CoNet in an interlaced combination to properly incorporate long-range dependencies and hierarchical object concepts at various scales. Furthermore, we employ the constricted attention bridge to constrict attention to local features, allowing us to accurately guide the recovery of detailed information while maintaining feature consistency. Finally, atrous spatial pyramid pooling is applied to high-level feature map to establish the concept of multi-scale objects. On five challenging datasets, including Synapse, ACDC, brain tumor segmentation, cardiac left atrium segmentation, and lung tumor segmentation, the extensive experiments demonstrate that TC-CoNet outperforms state-of-the-art approaches in terms of superiority, migration, and strong generalization. These illustrate in full the efficacy of the proposed transformers and convolutional neural networks combination for medical image segmentation. Our code is freely available at: https://github.com/YongChen-Exact/TC-CoNet.

Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images.

Liver segmentation is an essential procedure in computer-assisted surgery, radiotherapy, and volume measurement. It is still a challenging task to extract liver tissue from 3D CT images owing to nearby organs with similar intensities. In this paper, an automatic approach integrating multi-dimensional features into graph cut refinement is developed and validated. Multi-atlas segmentation is utilized to estimate the coarse shape of liver on the target image. The unsigned distance field based on initial shape is then calculated throughout the whole image, which aims at automatic graph construction during refinement procedure. Finally, multi-dimensional features and shape constraints are embedded into graph cut framework. The optimal liver region can be precisely detected with a minimal cost. The proposed technique is evaluated on 40 CT scans, obtained from two public databases Sliver07 and 3Dircadb1. The dataset Sliver07 is considered as the training set for parameter learning. On the dataset 3Dircadb1, the average of volume overlap is up to 94%. The experiment results indicate that the proposed method has ability to reach the desired boundary of liver and has potential value for clinical application.

Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images.

BACKGROUND: Dual-source computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images. METHODS: In this paper, we implement the heart segmentation-propagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of n-dimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the free-form deformation, in order to restrain the large variations and boundary ambiguity between subjects. RESULTS: We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 ([Formula: see text]) for left ventricle myocardium and from 0.6307 to 0.6519 ([Formula: see text]) for right atrium. CONCLUSIONS: The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.