Siqi Li
University of California, Davis
Deep learningImage segmentationPET-CTArtificial intelligenceRandom forestPattern recognitionSoftmax functionExtreme learning machineNucleusComputer visionComputer scienceContextual image classificationConvolutional neural networkSegmentationImage processingClassifier (UML)
23Publications
7H-index
121Citations
Publications 23
Newest
#1Siqi Li (UC Davis: University of California, Davis)H-Index: 7
#2Guobao Wang (UC Davis: University of California, Davis)H-Index: 19
Image reconstruction for positron emission tomography (PET) is challenging because of the ill-conditioned tomographic problem and low counting statistics. Kernel methods address this challenge by using kernel representation to incorporate image prior information in the forward model of iterative PET image reconstruction. Existing kernel methods construct the kernels commonly using an empirical process, which may lead to suboptimal performance. In this paper, we describe the equivalence between t...
#1Siqi Li (UC Davis: University of California, Davis)H-Index: 7
#2Guobao Wang (UC Davis: University of California, Davis)H-Index: 19
Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PET-enabled dual-energy CT combines a low-energy X-ray CT image with a high-energy -ray CT (G...
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#1Siqi LiH-Index: 7
#2Huiyan JiangH-Index: 10
Last. Yu-Dong Yao (Stevens Institute of Technology)H-Index: 38
view all 4 authors...
Accurate lymphoma segmentation on Positron Emission Tomography (PET) images is of great importance for medical diagnoses, such as for distinguishing benign and malignant. To this end, this paper proposes an adaptive weighting and scalable distance regularized level set evolution (AW-SDRLSE) method for delineating lymphoma boundaries on 2D PET slices. There are three important characteristics with respect to AW-SDRLSE: 1) A scalable distance regularization term is proposed and a parameter qcan...
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#1Siqi Li (UC Davis: University of California, Davis)H-Index: 7
#2Guobao Wang (UC Davis: University of California, Davis)H-Index: 19
The PET-enabled dual-energy CT method allows dual-energy CT imaging on PET/CT scanners without the need for a second x-ray CT scan. A 511 keV γ-ray attenuation image can be reconstructed from time-of-flight PET emission data using the maximum-likelihood attenuation and activity (MLAA) algorithm. However, the attenuation image reconstructed by standard MLAA is commonly noisy. To suppress noise, we propose a neuralnetwork approach for MLAA reconstruction. The PET attenuation image is described as ...
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#1Siqi Li (UC Davis: University of California, Davis)H-Index: 7
#2Guobao Wang (UC Davis: University of California, Davis)H-Index: 19
Deep neural networks have a great potential to improve image denoising in low-dose computed tomography (LDCT). Popular ways to increase the network capacity include adding more layers or repeating a modularized clone model in a sequence. In such sequential architectures, the noisy input image and end output image are commonly used only once in the training model, which however limits the overall learning performance. In this paper, we propose a parallel-clone neural network method that utilizes ...
#1Haoming Li (NU: Northeastern University)H-Index: 3
#2Huiyan Jiang (NU: Northeastern University)H-Index: 10
Last. Youchao Wang (Shenyang)H-Index: 1
view all 8 authors...
Automatic lymphoma detection and accurate lymphoma boundary delineation from whole body Positron Emission Tomography/Computed Tomography (PET/CT) scans are essential for surgical navigation and radiation therapy. Besides, labeling the data, which means contouring the lymphoma contour in images is time-consuming, operator intensive and subjective. Hence, this paper integrates the supervised learning and unsupervised learning to propose an end-to-end segmentation network, namely DenseX-Net, for bo...
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#4Ye Liu (SYSU: Sun Yat-sen University)H-Index: 2
Abstract Accurate nucleus detection is of great importance in pathological image analyses and diagnoses, which is a critical prerequisite for tasks such as automated grading hepatocellular carcinoma (HCC) nuclei. This paper proposes an automated nucleus detection framework based on a stacked sparse autoencoder (SSAE) and a case-based postprocessing method (CPM) in a coarse-to-fine manner. SSAE, an unsupervised learning model, is first trained using image patches of breast cancer. Then, the trans...
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#1Jitong ZhangH-Index: 1
#2Huiyan JiangH-Index: 10
Last. Siqi LiH-Index: 7
view all 5 authors...
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#1Zhiqi Bai (NU: Northeastern University)H-Index: 3
#2Huiyan Jiang (NU: Northeastern University)H-Index: 10
Last. Yu-Dong Yao (Stevens Institute of Technology)H-Index: 38
view all 4 authors...
Liver cancer is one of the most common cancers. Liver tumor segmentation is one of the most important steps in treating liver cancer. Accurate tumor segmentation on computed tomography (CT) images is a challenging task due to the variation of the tumor’s shape, size, and location. To this end, this paper proposes a liver tumor segmentation method on CT volumes using multi-scale candidate generation method (MCG), 3D fractal residual network (3D FRN), and active contour model (ACM) in a coarse-to-...
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#1Honglin Zhu (NU: Northeastern University)H-Index: 1
#2Huiyan Jiang (NU: Northeastern University)H-Index: 10
Last. Yan Pei (University of Aizu)H-Index: 12
view all 5 authors...
Pathological image classification is of great importance in various biomedical applications, such as for lesion detection, cancer subtype identification, and pathological grading. To this end, this paper proposed a novel classification framework using the multispace image reconstruction inputs and the transfer learning technology. Specifically, a multispace image reconstruction method was first developed to generate a new image containing three channels composed of gradient, gray level cooccurre...
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