Xiuying Wang

Bio: Xiuying Wang is an academic researcher from University of Sydney. The author has contributed to research in topics: Image segmentation & Image registration. The author has an hindex of 18, co-authored 139 publications receiving 1090 citations. Previous affiliations of Xiuying Wang include Heilongjiang University & Hong Kong Polytechnic University.

A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes

Abstract: In computed tomography of liver tumors there is often heterogeneous density, weak boundaries, and the liver tumors are surrounded by other abdominal structures with similar densities. These pose limitations to accurate the hepatic tumor segmentation. We propose a level set model incorporating likelihood energy with the edge energy. The minimization of the likelihood energy approximates the density distribution of the target and the multimodal density distribution of the background that can have multiple regions. In the edge energy formulation, our edge detector preserves the ramp associated with the edges for weak boundaries. We compared our approach to the Chan-Vese and the geodesic level set models and the manual segmentation performed by clinical experts. The Chan-Vese model was not successful in segmenting hepatic tumors and our model outperformed the geodesic level set model. Our results on 18 clinical datasets showed that our algorithm had a Jaccard distance error of 14.4 ± 5.3%, relative volume difference of -8.1 ± 2.1%, average surface distance of 2.4 ± 0.8 mm, RMS surface distance of 2.9 ± 0.7 mm, and the maximum surface distance of 7.2 ± 3.1 mm.

Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.

Abstract: Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management.

Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

Abstract: Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of...

Long Noncoding RNA SChLAP1 Forms a Growth-Promoting Complex with HNRNPL in Human Glioblastoma through Stabilization of ACTN4 and Activation of NF-κB Signaling.

Abstract: Purpose: Long noncoding RNAs (lncRNA) have essential roles in diverse cellular processes, both in normal and diseased cell types, and thus have emerged as potential therapeutic targets. A specific member of this family, the SWI/SNF complex antagonist associated with prostate cancer 1 (SChLAP1), has been shown to promote aggressive prostate cancer growth by antagonizing the SWI/SNF complex and therefore serves as a biomarker for poor prognosis. Here, we investigated whether SChLAP1 plays a potential role in the development of human glioblastoma (GBM). Experimental Design: RNA-ISH and IHC were performed on a tissue microarray to assess expression of SChLAP1 and associated proteins in human gliomas. Proteins complexed with SChLAP1 were identified using RNA pull-down and mass spectrometry. Lentiviral constructs were used for functional analysis in vitro and in vivo. Results: SChLAP1 was increased in primary GBM samples and cell lines, and knockdown of the lncRNA suppressed growth. SChLAP1 was found to bind heterogeneous nuclear ribonucleoprotein L (HNRNPL), which stabilized the lncRNA and led to an enhanced interaction with the protein actinin alpha 4 (ACTN4). ACTN4 was also highly expressed in primary GBM samples and was associated with poorer overall survival in glioma patients. The SChLAP1–HNRNPL complex led to stabilization of ACTN4 through suppression of proteasomal degradation, which resulted in increased nuclear localization of the p65 subunit of NF-κB and activation of NF-κB signaling, a pathway associated with cancer development. Conclusions: Our results implicated SChLAP1 as a driver of GBM growth as well as a potential therapeutic target in treatment of the disease.

A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data

Abstract: Clustering is increasingly important for multiview data analytics and current algorithms are either based on the collaborative learning of local partitions or directly derived global clustering from multikernel learning. In this paper, we innovate a clustering model that unifies the local partitions and global clustering in a collaborative learning framework. We first construct a common multikernel space from a set of basis kernels to better reflect clustering information of each individual view. Then, considering that joint local partitions would conform to the global clustering, we fuse the local partitions and global clustering guidance as a single objective function in accordance with fuzzy clustering form. The collaborative learning strategy enables the mutual and interactive clustering from local partitions and global clustering. The validation was performed over two synthetic and four public databases and the clustering accuracy was measured by normalized mutual information and rand index. The experimental results demonstrated that the proposed algorithm outperformed the related state-of-the-art algorithms in comparison, which included multitask, multikernel, and multiview clustering approaches.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Abstract: Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

Deformable Medical Image Registration: A Survey

Abstract: Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.