arXiv subjects

Xiaoxiao He

Publications and source records attributed to Xiaoxiao He.

22 records · Page 2Linked to original sources

arXiv2022

TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semantic dependency mining, addressing the critical issue of capturing long-range correlations between unaligned data from different image views. We further propose the Multi-Scale Attention (MSA) to collect global correspondence of multi-scale feature representations. We evaluate TransFusion on the Multi-Disease, Multi-View \& Multi-Center Right Ventricular Segmentation in Cardiac MRI (M\&Ms-2) challenge cohort. TransFusion demonstrates leading performance against the state-of-the-art methods and opens up new perspectives for multi-view imaging integration towards robust medical image segmentation.

eess.IV↗

arXiv2022

Recursive 3D Segmentation of Shoulder Joint with Coarse-scanned MR Image

For diagnosis of shoulder illness, it is essential to look at the morphology deviation of scapula and humerus from the medical images that are acquired from Magnetic Resonance (MR) imaging. However, taking high-resolution MR images is time-consuming and costly because the reduction of the physical distance between image slices causes prolonged scanning time. Moreover, due to the lack of training images, images from various sources must be utilized, which creates the issue of high variance across the dataset. Also, there are human errors among the images due to the fact that it is hard to take the spatial relationship into consideration when labeling the 3D image in low resolution. In order to combat all obstacles stated above, we develop a fully automated algorithm for segmenting the humerus and scapula bone from coarsely scanned and low-resolution MR images and a recursive learning framework that iterative utilize the generated labels for reducing the errors among segmentations and increase our dataset set for training the next round network. In this study, 50 MR images are collected from several institutions and divided into five mutually exclusive sets for carrying five-fold cross-validation. Contours that are generated by the proposed method demonstrated a high level of accuracy when compared with ground truth and the traditional method. The proposed neural network and the recursive learning scheme improve the overall quality of the segmentation on humerus and scapula on the low-resolution dataset and reduced incorrect segmentation in the ground truth, which could have a positive impact on finding the cause of shoulder pain and patient's early relief.

eess.IV↗

arXiv2021

A stabilized nonconforming Nitsche's extended finite element method for Stokes interface problems

In this paper, a stabilized extended finite element method is proposed for Stokes interface problems on unfitted triangulation elements which do not require the interface align with the triangulation. The velocity solution and pressure solution on each side of the interface are separately expanded in the standard nonconforming piecewise linear polynomials and the piecewise constant polynomials, respectively. Harmonic weighted fluxes and arithmetic fluxes are used across the interface and cut edges (segment of the edges cut by the interface), respectively. Extra stabilization terms involving velocity and pressure are added to ensure the stable inf-sup condition. We show a priori error estimates under additional regularity hypothesis. Moreover, the errors {in energy and $L^2$ norms for velocity and the error in $L^2$ norm for pressure} are robust with respect to the viscosity {and independent of the location of the interface}. Results of numerical experiments are presented to {support} the theoretical analysis.

math.NA↗

arXiv2019

Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data

For the initial shoulder preoperative diagnosis, it is essential to obtain a three-dimensional (3D) bone mask from medical images, e.g., magnetic resonance (MR). However, obtaining high-resolution and dense medical scans is both costly and time-consuming. In addition, the imaging parameters for each 3D scan may vary from time to time and thus increase the variance between images. Therefore, it is practical to consider the bone extraction on low-resolution data which may influence imaging contrast and make the segmentation work difficult. In this paper, we present a joint segmentation for the humerus and scapula bones on a small dataset with low-contrast and high-shape-variability 3D MR images. The proposed network has a deep end-to-end architecture to obtain the initial 3D bone masks. Because the existing scarce and inaccurate human-labeled ground truth, we design a self-reinforced learning strategy to increase performance. By comparing with the non-reinforced segmentation and a classical multi-atlas method with joint label fusion, the proposed approach obtains better results.

cs.CV↗