Huazhong Shu
Southeast University
Deep learningAlgorithmImage (mathematics)Mathematical optimizationIterative reconstructionVelocity MomentsArtificial intelligencePattern recognitionQuaternionComputer visionMathematicsComputer scienceComputationFeature extractionMedicineFeature (computer vision)SegmentationImage processingRobustness (computer science)
280Publications
34H-index
4,017Citations
Publications 276
Newest
#1Weiya Sun (SEU: Southeast University)H-Index: 1
#2Guanyu Yang (SEU: Southeast University)H-Index: 15
Last. Huazhong Shu (SEU: Southeast University)H-Index: 34
view all 4 authors...
BACKGROUND The determination of the right x-ray angiography viewing angle is an important issue during the treatment of thoracic endovascular aortic repair (TEVAR). An inaccurate projection angle (manually determined today by the physicians according to their personal experience) may affect the placement of the stent and cause vascular occlusion or endoleak. METHODS Based on the acquisition of a computed tomography angiography (CTA) image before TEVAR, an adaptive optimization algorithm is propo...
Source
#1Yan ZhangH-Index: 129
#2Li YifeiH-Index: 1
Last. Gouenou Coatrieux (French Institute of Health and Medical Research)H-Index: 27
view all 7 authors...
Abstract In this paper, we propose a graph self-construction and fusion network (GSCFN) for semi-supervised brain tissue segmentation in Magnetic Resonance Imaging (MRI) by fusing multiple types of image features. Compared to the use of a single feature, various features bring complementary information and can contribute to a better graph representation with a great discriminative power increase. But to do so, two problems need to be solved. The first one consists in effectively inferring a grap...
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#4Yang ChenH-Index: 27
#4Huazhong Shu (SEU: Southeast University)H-Index: 34
Last. Jean-Louis Coatrieux (University of Rennes)H-Index: 25
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BACKGROUND AND OBJECTIVE Aortic dissection is a severe cardiovascular pathology in which an injury of the intimal layer of the aorta allows blood flowing into the aortic wall, forcing the wall layers apart. Such situation presents a high mortality rate and requires an in-depth understanding of the 3-D morphology of the dissected aorta to plan the right treatment. An accurate automatic segmentation algorithm is therefore needed. METHOD In this paper, we propose a deep-learning-based algorithm to ...
Source
#1Shuo Li (UWO: University of Western Ontario)H-Index: 39
#2Yuting He (SEU: Southeast University)H-Index: 2
Last. Huazhong Shu (SEU: Southeast University)H-Index: 34
view all 9 authors...
Deformable medical image registration estimates corresponding deformation to align the regions of interest (ROIs) of two images to a same spatial coordinate system. However, recent unsupervised registration models only have correspondence ability without perception, making misalignment on blurred anatomies and distortion on task-unconcerned backgrounds. Label-constrained (LC) registration models embed the perception ability via labels, but the lack of texture constraints in labels and the expens...
Source
#1Yuting He (SEU: Southeast University)H-Index: 2
#2Rongjun Ge (SEU: Southeast University)H-Index: 4
Last. Shuo Li (UWO: University of Western Ontario)H-Index: 39
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3D complete renal structures(CRS) segmentation targets on segmenting the kidneys, tumors, renal arteries and veins in one inference. Once successful, it will provide preoperative plans and intraoperative guidance for laparoscopic partial nephrectomy(LPN), playing a key role in the renal cancer treatment. However, no success has been reported in 3D CRS segmentation due to the complex shapes of renal structures, low contrast and large anatomical variation. In this study, we utilize the adversarial...
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Jun 6, 2021 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Li Liu (Donghua University)
#2Da Chen (Qilu University of Technology)H-Index: 8
Last. Laurent D. Cohen (University of Paris)H-Index: 99
view all 5 authors...
Source
#1Yuting He (SEU: Southeast University)H-Index: 2
#2Guanyu Yang (SEU: Southeast University)H-Index: 15
Last. Shuo Li (UWO: University of Western Ontario)H-Index: 39
view all 12 authors...
Abstract Three-dimensional (3D) integrated renal structures (IRS) segmentation targets segmenting the kidneys, renal tumors, arteries, and veins in one inference. Clinicians will benefit from the 3D IRS visual model for accurate preoperative planning and intraoperative guidance of laparoscopic partial nephrectomy (LPN). However, no success has been reported in 3D IRS segmentation due to the inherent challenges in grayscale distribution: low contrast caused by the narrow task-dependent distributi...
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#1Weiya Sun (SEU: Southeast University)H-Index: 1
#2Huanming Xu (BIT: Beijing Institute of Technology)H-Index: 4
Last. Duanduan Chen (BIT: Beijing Institute of Technology)H-Index: 13
view all 8 authors...
Abstract Purpose : Stanford type-B aortic dissection (TBAD) is commonly treated by thoracic endovascular aortic repair (TEVAR). Usually, the implanted stent-grafts will not cover the entire dissection-affected region for those patients with dissection extending beyond the thoracic aorta, thus the fate of the uncovered aortic segment is uncertain. This study used three-dimensional measurement of aortic morphological changes to classify the different remodeling effects of TBAD patients after TEVAR...
1 CitationsSource
#1Jiasong WuH-Index: 16
#2Fuzhi WuH-Index: 1
Last. Huazhong ShuH-Index: 34
view all 8 authors...
One of the key challenges in the area of signal processing on graphs is to design transforms and dictionary methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize graph Fourier transform (GFT) to spectral graph fractional Fourier transform (SGFRFT), which is then used to define a novel transform named spectral graph fractional wavelet transform (SGFRWT), which is a generalized and extended version of spectral graph wavelet transform (SGWT). A ...
3 CitationsSource
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation. However, further adoption of the joint models are hindered: 1) the diversity of augmentation data is reduced limiting the further enhancement of segmentation, 2) misaligned regions in weakly supervised data disturb the train...
2 Citations