Limin Luo
Southeast University
AlgorithmImage (mathematics)Image segmentationIterative reconstructionNoiseArtificial intelligencePixelTomographyPattern recognitionProjection (set theory)Computer visionMathematicsComputer scienceComputationFeature extractionComputed tomographyImage qualityFeature (computer vision)SegmentationImage processing
Publications 171
2 CitationsSource
#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 ...
#2Yikun ZhangH-Index: 2
Last. Yang Chen (SEU: Southeast University)H-Index: 27
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#1Dianlin Hu (SEU: Southeast University)H-Index: 4
#2Yikun Zhang (SEU: Southeast University)H-Index: 2
Last. Limin Luo (SEU: Southeast University)H-Index: 29
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#1Haichen Zhu (SEU: Southeast University)H-Index: 1
#2Liang Jiang (Nanjing Medical University)H-Index: 6
Last. Yu-Chen Chen (Nanjing Medical University)H-Index: 12
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Abstract null null Current thrombolysis for acute ischemic stroke (AIS) treatment strictly relies on the time since stroke (TSS) less than 4.5h. However, some patients are excluded from thrombolytic treatment because of the unknown TSS. The diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch can simply identify TSS since lesion intensities are not identical at different onset time. In this paper, we propose an automatic machine learning method to classify th...
#1Tianling Lyu (SEU: Southeast University)H-Index: 1
#2Wei Zhao (Stanford University)H-Index: 36
Last. Lei Xing (Stanford University)H-Index: 85
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Abstract Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DEC...
4 CitationsSource
X-ray computed tomography (CT) is one of the most widely used tools in medical imaging, industrial nondestructive testing, lesion detection, and other applications. However, decreasing the projection number to lower the X-ray radiation dose usually leads to severe streak artifacts. To improve the quality of the images reconstructed from sparse-view projection data, we developed a hybrid-domain neural network (HDNet) processing for sparse-view CT (SVCT) reconstruction in this study. The HDNet dec...
8 CitationsSource
#1Zhan Wu (SEU: Southeast University)
#2Rongjun Ge (SEU: Southeast University)H-Index: 4
Last. Hengyong Yu (University of Massachusetts Lowell)H-Index: 37
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Pulmonary nodule false positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computerized tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in the LDCT images, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues. In this paper, we propose a multi-dimension nodule detect...
5 CitationsSource
#6Gouenou Coatrieux (École nationale supérieure des télécommunications de Bretagne)H-Index: 27
#7Jian Yang (BIT: Beijing Institute of Technology)H-Index: 181
Last. Shuo Li (UWO: University of Western Ontario)H-Index: 39
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Abstract Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet) is proposed as an automatic clinical tool to classify five stages of DR severity g...
10 CitationsSource
#1Rongjun Ge (SEU: Southeast University)H-Index: 4
#2Guanyu Yang (SEU: Southeast University)H-Index: 15
Last. Shuo Li (UWO: University of Western Ontario)H-Index: 39
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The integration of segmentation and direct quantification on the left ventricle (LV) from the paired apical views(i.e., apical 4-chamber and 2-chamber together) echo sequence clinically achieves the comprehensive cardiac assessment: multiview segmentation for anatomical morphology, and multidimensional quantification for contractile function. Direct quantification of LV, i.e., to automatically quantify multiple LV indices directly from the image via task-aware feature representation and regressi...
21 CitationsSource