Mr. Zibi Xu Profile

Zibi Xu

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Proceedings Article | 25 March 2024 Paper

Dual-path U-transformer network for assessment of brain midline shift in hemorrhagic stroke

Jinhuang Zhang, Wei Liang, Hongbin Guo, Huiling Zhang, Zibi Xu, Xiangyuan Ma

Proceedings Volume 13089, 1308910 (2024) https://doi.org/10.1117/12.3019574

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Midline shift is an important clinical indicator of the severity of hemorrhagic stroke and holds significance in a physician's clinical diagnosis. Segmentation-based methods for midline shift assessment are prevalent in the field, but the full utilization of global information within network remains a challenge. In addition, empirical integration with clinical knowledge is also essential. In this study, we developed a two-stage method for automatic midline shift assessment. Firstly, in the midline identification step, we proposed a Dual-Path U-Transformer to segment the brain midline. The Dual-Path U-Transformer can better capitalize on global information by integrating self-attention mechanism, while still retaining the characteristics of U-Net in making full use of local information and combining high and low dimensional features. In the second stage, according to clinical knowledge learned from clinical expert, we calculated the maximum shift distance for assessment of brain midline shift, determining whether each case has surgical indication. In the experiments process, we use 5-fold cross validation to train and validate the proposed model. Compared with traditional U-Net based method and transformer-based method, the proposed Dual-Path U-Transformer based method performed the best HD and Dice performance on our inhouse dataset. And the experiment results confirmed that the Dual-Path U-Transformer based exhibited excellent accuracy in the second stage of midline shift assessment.

Proceedings Article | 27 June 2023 Paper

Adaptive scale based u-shape transformer network for ischemic stroke lesion segmentation in CTP images

Huiling Zhang, Wencong Zhang, Yingjia Chen, Zibi Xu, Xiangyuan Ma

Proceedings Volume 12705, 127051Q (2023) https://doi.org/10.1117/12.2680047

KEYWORDS: Image segmentation, Transformers, Ischemic stroke, Computed tomography, Medical imaging

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Ischemic stroke lesion segmentation in Computed Tomography Perfusion (CTP) images is crucial for the quantitative diagnosis of stroke. However, it remains a challenging problem due to the poor image quality of CTP and the complex appearance of the lesions. In this study, we develop a U-shape transformer network with an adaptive scale for ischemic stroke lesion segmentation in CTP images. The state-of-the-art nnU-Net structure is used as the backbone, and a transformer block with self-adapting scale is introduced. The proposed network adopts the advantage of transformer in capturing global information and retains the advantage of convolutional neural network (CNN) in extracting local correlation features. In order to obtain better adaptation of transformer block to ischemic stroke segmentation task, we propose a self-adapting scale selection strategy that offer better patch size and window size to assist the transformer block capture more global information and avoid semantic information being corrupted. Five-fold cross-validation was used in training of the networks, and nnUNet was used as a baseline model in the performance evaluation. The results showed that after involving the proposed method, the mean DICE of the segmentation improved from 0.72 to 0.78 in the ISLES public dataset. For the independent test set, the proposed method achieved a mean DICE of 0.48, a mean precision of 0.60, and a mean recall of 0.46, compared to 0.46, 0.57 and 0.43 by the baseline model. The proposed framework has the potential for improving diagnosis and treatment of ischemic stroke in CTP.

Proceedings Article | 27 June 2023 Paper

GCN-based group level analysis of brain functional connectivity in fMRI

Xiangyuan Ma, Zhisheng Deng, Zibi Xu, Yaqin Zhang

Proceedings Volume 12705, 127050Y (2023) https://doi.org/10.1117/12.2680074

KEYWORDS: Brain, Functional magnetic resonance imaging, Traumatic brain injury, Brain diseases, Statistical analysis

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Brain functional network describes the functional connectivity (FC) between brain regions, and hence provides a crucial way for analyzing brain diseases. In order to explore neural mechanism of a brain disease, statistical test method is usually used to obtain the FC differences between normal group and abnormal group. However, it is difficult for statistical test method to utilize features from brain region nodes and brain connection edges simultaneously. In this study, we develop a method based on graph convolution network (GCN) for brain functional connectivity analysis in functional magnetic resonance imaging (fMRI). Graph convolution is used to extract the features from brain region nodes and brain connection edges simultaneously, and the interpretability of GCN is applied to obtain the FC differences between different groups. The proposed method is able to analyze the brain functional connectivity more comprehensively and can be a supplement to traditional statistical test method. A task-state public fMRI data set including healthy group, severe traumatic brain injury (TBI) patient group was used for training and testing of the models. And a statistical test method was used as baseline in the performance evaluation. The results showed that the proposed GCN-based method outperformed the statistical baseline method. This method has potential to find more useful FC when we analyzing the neural mechanisms of brain diseases.

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