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Li Y, Lin C, Zhang Y, Feng S, Huang M, Bai Z. Automatic segmentation of prostate MRI based on 3D pyramid pooling Unet. Med Phys 2023; 50:906-921. [PMID: 35923153 DOI: 10.1002/mp.15895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 06/23/2022] [Accepted: 06/25/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Automatic segmentation of prostate magnetic resonance (MR) images is crucial for the diagnosis, evaluation, and prognosis of prostate diseases (including prostate cancer). In recent years, the mainstream segmentation method for the prostate has been converted to convolutional neural networks. However, owing to the complexity of the tissue structure in MR images and the limitations of existing methods in spatial context modeling, the segmentation performance should be improved further. METHODS In this study, we proposed a novel 3D pyramid pool Unet that benefits from the pyramid pooling structure embedded in the skip connection (SC) and the deep supervision (DS) in the up-sampling of the 3D Unet. The parallel SC of the conventional 3D Unet network causes low-resolution information to be sent to the feature map repeatedly, resulting in blurred image features. To overcome the shortcomings of the conventional 3D Unet, we merge each decoder layer with the feature map of the same scale as the encoder and the smaller scale feature map of the pyramid pooling encoder. This SC combines the low-level details and high-level semantics at two different levels of feature maps. In addition, pyramid pooling performs multifaceted feature extraction on each image behind the convolutional layer, and DS learns hierarchical representations from comprehensive aggregated feature maps, which can improve the accuracy of the task. RESULTS Experiments on 3D prostate MR images of 78 patients demonstrated that our results were highly correlated with expert manual segmentation. The average relative volume difference and Dice similarity coefficient of the prostate volume area were 2.32% and 91.03%, respectively. CONCLUSION Quantitative experiments demonstrate that, compared with other methods, the results of our method are highly consistent with the expert manual segmentation.
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Affiliation(s)
- Yuchun Li
- State Key Laboratory of Marine Resource Utilization in South China Sea, School of information and Communication Engineering, Hainan University, Haikou, China
| | - Cong Lin
- State Key Laboratory of Marine Resource Utilization in South China Sea, School of information and Communication Engineering, Hainan University, Haikou, China.,College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China
| | - Yu Zhang
- College of Computer science and Technology, Hainan University, Haikou, China
| | - Siling Feng
- State Key Laboratory of Marine Resource Utilization in South China Sea, School of information and Communication Engineering, Hainan University, Haikou, China
| | - Mengxing Huang
- State Key Laboratory of Marine Resource Utilization in South China Sea, School of information and Communication Engineering, Hainan University, Haikou, China
| | - Zhiming Bai
- Haikou Municipal People's Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, China
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Liu F, Zhao L, Lu YC, Wu Y. Editorial: Innovative applications with artificial intelligence methods in neuroimaging data analysis. Front Hum Neurosci 2022; 16:1108253. [PMID: 36590064 PMCID: PMC9800991 DOI: 10.3389/fnhum.2022.1108253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Affiliation(s)
- Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yuan-Chiao Lu
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - Yao Wu
- Developing Brain Institute, Children's National Hospital, Washington, DC, United States,*Correspondence: Yao Wu, ✉
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Wang S, Liu M, Lian J, Shen D. Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:310-320. [PMID: 32956051 PMCID: PMC8202780 DOI: 10.1109/tmi.2020.3025517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for organ boundary and use it as the context information to guide the segmentation. Specifically, we design a two-stage learning strategy in the proposed BCnet: 1) Boundary coding representation learning. Two sub-networks under the supervision of the dilation and erosion masks transformed from the manually delineated organ mask are first separately trained to learn the spatial-semantic context near the organ boundary. Then we encode the organ boundary based on the predictions of these two sub-networks and design a multi-atlas based refinement strategy by transferring the knowledge from training data to inference. 2) Organ segmentation. The boundary coding representation as context information, in addition to the image patches, are used to train the final segmentation network. Experimental results on a large and diverse male pelvic CT dataset show that our method achieves superior performance compared with several state-of-the-art methods.
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Zhu H, Tang Z, Cheng H, Wu Y, Fan Y. Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation. Sci Rep 2019; 9:16839. [PMID: 31727982 PMCID: PMC6856174 DOI: 10.1038/s41598-019-53387-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 10/30/2019] [Indexed: 01/15/2023] Open
Abstract
Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies. To improve the hippocampus segmentation performance, a local binary pattern based feature extraction method is developed for machine learning based multi-atlas hippocampus segmentation. Under the framework of multi-atlas image segmentation (MAIS), a set of selected atlases are registered to images to be segmented using a non-linear image registration algorithm. The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted using a novel image feature extraction method. The RLBP based MAIS algorithm has been validated for segmenting hippocampus based on a data set of 135 T1 MR images which are from the Alzheimer’s Disease Neuroimaging Initiative database (adni.loni.usc.edu). By using manual segmentation labels produced by experienced tracers as the standard of truth, six segmentation evaluation metrics were used to evaluate the image segmentation results by comparing automatic segmentation results with the manual segmentation labels. We further computed Cohen’s d effect size to investigate the sensitivity of each segmenting method in detecting volumetric differences of the hippocampus between different groups of subjects. The evaluation results showed that our method was competitive to state-of-the-art label fusion methods in terms of accuracy. Hippocampal volumetric analysis showed that the proposed RLBP method performed well in detecting the volumetric differences of the hippocampus between groups of Alzheimer’s disease patients, mild cognitive impairment subjects, and normal controls. These results have demonstrated that the RLBP based multi-atlas image segmentation method could facilitate efficient and accurate extraction of the hippocampus and may help predict Alzheimer’s disease. The codes of the proposed method is available (https://www.nitrc.org/frs/?group_id=1242).
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Affiliation(s)
- Hancan Zhu
- School of Mathematics Physics and Information, Shaoxing University, Shaoxing, Zhejiang, 312000, China
| | - Zhenyu Tang
- Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China
| | - Hewei Cheng
- Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Yihong Wu
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Pang S, Lu Z, Jiang J, Zhao L, Lin L, Li X, Lian T, Huang M, Yang W, Feng Q. Hippocampus Segmentation Based on Iterative Local Linear Mapping With Representative and Local Structure-Preserved Feature Embedding. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2271-2280. [PMID: 30908202 DOI: 10.1109/tmi.2019.2906727] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Hippocampus segmentation plays a significant role in mental disease diagnoses, such as Alzheimer's disease, epilepsy, and so on. Patch-based multi-atlas segmentation (PBMAS) approach is a popular method for hippocampus segmentation and has achieved a promising result. However, the PBMAS approach needs high computation cost due to registration and the segmentation accuracy is subject to the registration accuracy. In this paper, we propose a novel method based on iterative local linear mapping (ILLM) with the representative and local structure-preserved feature embedding to achieve accurate and robust hippocampus segmentation with no need for registration. In the proposed approach, semi-supervised deep autoencoder (SSDA) exploits unsupervised deep autoencoder and local structure-preserved manifold regularization to nonlinearly transform the extracted magnetic resonance (MR) patch to embedded feature manifold, whose adjacent relationship is similar to the signed distance map (SDM) patch manifold. Local linear mapping is used to preliminarily predict SDM patch corresponding to the MR patch. Subsequently, threshold segmentation generates a preliminary segmentation. The ILLM refines the segmentation result iteratively by ensuring the local constraints of embedded feature manifold and SDM patch manifold using a space-constrained dictionary update. Thus, a refined segmentation is obtained with no need for registration. The experiments on 135 subjects from ADNI dataset show that the proposed approach is superior to the state-of-the-art PBMAS and classification-based approaches with mean Dice similarity coefficients of 0.8852±0.0203 and 0.8783 ± 0.0251 for bilateral hippocampus segmentation of 1.5T and 3.0T datasets, respectively.
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Tang Z, Wang M, Song Z. Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory. Phys Med 2018; 54:56-65. [PMID: 30337011 DOI: 10.1016/j.ejmp.2018.09.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 09/16/2018] [Accepted: 09/18/2018] [Indexed: 11/24/2022] Open
Abstract
PURPOSE In this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate. METHODS Our method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic. RESULTS We tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training. CONCLUSION The proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.
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Affiliation(s)
- Zhixian Tang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Manning Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China.
| | - Zhijian Song
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China.
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Yang G, Zhuang X, Khan H, Haldar S, Nyktari E, Li L, Wage R, Ye X, Slabaugh G, Mohiaddin R, Wong T, Keegan J, Firmin D. Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI. Med Phys 2018; 45:1562-1576. [PMID: 29480931 PMCID: PMC5969251 DOI: 10.1002/mp.12832] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 02/01/2018] [Accepted: 02/17/2018] [Indexed: 01/18/2023] Open
Abstract
PURPOSE Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed noninvasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualized as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time consuming and subject to interoperator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF. METHODS Our fully automatic pipeline uniquely combines: (a) a multiatlas-based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (b) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground truth segmentations in 37 patients with persistent long-standing AF. RESULTS Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%), respectively, compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91. CONCLUSION Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localization, visualization, and quantitation of atrial scarring and to guide ablation treatment.
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Affiliation(s)
- Guang Yang
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
- National Heart and Lung InstituteImperial College LondonLondonSW7 2AZUK
| | - Xiahai Zhuang
- School of Data ScienceFudan UniversityShanghai201203China
| | - Habib Khan
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
| | - Shouvik Haldar
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
| | - Eva Nyktari
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
| | - Lei Li
- Department of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Ricardo Wage
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
| | - Xujiong Ye
- School of Computer ScienceUniversity of LincolnLincolnLN6 7TSUK
| | - Greg Slabaugh
- Department of Computer ScienceCity University LondonLondonEC1V 0HBUK
| | - Raad Mohiaddin
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
- National Heart and Lung InstituteImperial College LondonLondonSW7 2AZUK
| | - Tom Wong
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
| | - Jennifer Keegan
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
- National Heart and Lung InstituteImperial College LondonLondonSW7 2AZUK
| | - David Firmin
- Cardiovascular Research CentreRoyal Brompton HospitalLondonSW3 6NPUK
- National Heart and Lung InstituteImperial College LondonLondonSW7 2AZUK
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Cherukuri V, Ssenyonga P, Warf BC, Kulkarni AV, Monga V, Schiff SJ. Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans. IEEE Trans Biomed Eng 2017; 65:1871-1884. [PMID: 29989926 DOI: 10.1109/tbme.2017.2783305] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed. METHODS Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images. RESULTS Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.
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Ma L, Guo R, Zhang G, Schuster DM, Fei B. A combined learning algorithm for prostate segmentation on 3D CT images. Med Phys 2017; 44:5768-5781. [PMID: 28834585 DOI: 10.1002/mp.12528] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 07/17/2017] [Accepted: 07/28/2017] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. METHODS We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. RESULTS The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. CONCLUSIONS By combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate.
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Affiliation(s)
- Ling Ma
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Rongrong Guo
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Guoyi Zhang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - David M Schuster
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.,Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.,Winship Cancer Institute of Emory University, Atlanta, GA, USA.,Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA
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Hippocampus Segmentation Based on Local Linear Mapping. Sci Rep 2017; 7:45501. [PMID: 28368016 PMCID: PMC5377362 DOI: 10.1038/srep45501] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 03/01/2017] [Indexed: 01/18/2023] Open
Abstract
We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.
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Tian Z, Liu L, Zhang Z, Xue J, Fei B. A supervoxel-based segmentation method for prostate MR images. Med Phys 2017; 44:558-569. [PMID: 27991675 DOI: 10.1002/mp.12048] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 12/02/2016] [Accepted: 12/07/2016] [Indexed: 01/22/2023] Open
Abstract
PURPOSE Segmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel-based segmentation method for prostate MR images. METHODS A supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume. The prostate segmentation problem is considered as assigning a binary label to each supervoxel, which is either the prostate or background. A supervoxel-based energy function with data and smoothness terms is used to model the label. The data term estimates the likelihood of a supervoxel belonging to the prostate by using a supervoxel-based shape feature. The geometric relationship between two neighboring supervoxels is used to build the smoothness term. The 3D graph cut is used to minimize the energy function to get the labels of the supervoxels, which yields the prostate segmentation. A 3D active contour model is then used to get a smooth surface by using the output of the graph cut as an initialization. The performance of the proposed algorithm was evaluated on 30 in-house MR image data and PROMISE12 dataset. RESULTS The mean Dice similarity coefficients are 87.2 ± 2.3% and 88.2 ± 2.8% for our 30 in-house MR volumes and the PROMISE12 dataset, respectively. The proposed segmentation method yields a satisfactory result for prostate MR images. CONCLUSION The proposed supervoxel-based method can accurately segment prostate MR images and can have a variety of application in prostate cancer diagnosis and therapy.
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Affiliation(s)
- Zhiqiang Tian
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University, 1841 Clifton Road NE, Atlanta, GA, 30329, USA
| | - Lizhi Liu
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University, 1841 Clifton Road NE, Atlanta, GA, 30329, USA.,Center for Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, 510060, P. R., China
| | - Zhenfeng Zhang
- Center for Medical Imaging and Image-guided Therapy, Sun Yat-Sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, 510060, P. R., China
| | - Jianru Xue
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, No.28 Xianning West Road, Xi'an, 710049, P. R., China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University, 1841 Clifton Road NE, Atlanta, GA, 30329, USA.,Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1841 Clifton Road NE, Atlanta, GA, 30329, USA
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12
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Song Y, Li Q, Zhang F, Huang H, Feng D, Wang Y, Chen M, Cai W. Dual discriminative local coding for tissue aging analysis. Med Image Anal 2017; 38:65-76. [PMID: 28282641 DOI: 10.1016/j.media.2016.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Revised: 07/12/2016] [Accepted: 10/05/2016] [Indexed: 11/26/2022]
Abstract
In aging research, morphological age of tissue helps to characterize the effects of aging on different individuals. While currently manual evaluations are used to estimate morphological ages under microscopy, such operation is difficult and subjective due to the complex visual characteristics of tissue images. In this paper, we propose an automated method to quantify morphological ages of tissues from microscopy images. We design a new sparse representation method, namely dual discriminative local coding (DDLC), that classifies the tissue images into different chronological ages. DDLC in- corporates discriminative distance learning and dual-level local coding into the basis model of locality-constrained linear coding thus achieves higher discriminative capability. The morphological age is then computed based on the classification scores. We conducted our study using the publicly avail- able terminal bulb aging database that has been commonly used in existing microscopy imaging research. To represent these images, we also design a highly descriptive descriptor that combines several complementary texture features extracted at two scales. Experimental results show that our method achieves significant improvement in age classification when compared to the existing approaches and other popular classifiers. We also present promising results in quantification of morphological ages.
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Affiliation(s)
- Yang Song
- School of Information Technologies, University of Sydney, Australia.
| | - Qing Li
- School of Information Technologies, University of Sydney, Australia
| | - Fan Zhang
- School of Information Technologies, University of Sydney, Australia
| | - Heng Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, USA
| | - Dagan Feng
- School of Information Technologies, University of Sydney, Australia; Med-X Research Institute, Shanghai Jiaotong University, China
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, USA
| | - Mei Chen
- Computer Engineering Department, University of Albany State University of New York, USA; Robotics Institute, Carnegie Mellon University, USA
| | - Weidong Cai
- School of Information Technologies, University of Sydney, Australia
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Ma L, Guo R, Zhang G, Tade F, Schuster DM, Nieh P, Master V, Fei B. Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10133. [PMID: 30220767 DOI: 10.1117/12.2255755] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.
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Affiliation(s)
- Ling Ma
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.,School of Computer Science, Beijing Institute of Technology
| | - Rongrong Guo
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Guoyi Zhang
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Funmilayo Tade
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - David M Schuster
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Peter Nieh
- Department of Urology, Emory University, Atlanta, GA
| | - Viraj Master
- Department of Urology, Emory University, Atlanta, GA
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.,Winship Cancer Institute of Emory University, Atlanta, GA.,The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, GA
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14
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Yao J, Yu H, Hu R. Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2017; 34:27-38. [PMID: 28059221 DOI: 10.1364/josaa.34.000027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.
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15
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Abstract
Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.
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Affiliation(s)
- Hancan Zhu
- School of Mathematics Physics and Information, Shaoxing University, Shaoxing, 312000, China
| | - Hewei Cheng
- Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Xuesong Yang
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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16
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Zhuang X, Shen J. Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med Image Anal 2016; 31:77-87. [PMID: 26999615 DOI: 10.1016/j.media.2016.02.006] [Citation(s) in RCA: 147] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/30/2015] [Accepted: 02/22/2016] [Indexed: 01/18/2023]
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17
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Wu Y, Yang W, Lu L, Lu Z, Zhong L, Huang M, Feng Y, Feng Q, Chen W. Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction. J Nucl Med 2016; 57:1635-1641. [PMID: 27230932 DOI: 10.2967/jnumed.115.163121] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 04/16/2016] [Indexed: 11/16/2022] Open
Affiliation(s)
- Yao Wu
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wei Yang
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Lijun Lu
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhentai Lu
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Liming Zhong
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Meiyan Huang
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianjin Feng
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wufan Chen
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
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18
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Ma L, Guo R, Tian Z, Venkataraman R, Sarkar S, Liu X, Tade F, Schuster DM, Fei B. Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9784:978427. [PMID: 27660382 PMCID: PMC5029417 DOI: 10.1117/12.2216255] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.
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Affiliation(s)
- Ling Ma
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
- School of Computer Science, Beijing Institute of Technology, Beijing
| | - Rongrong Guo
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | | | | | - Xiabi Liu
- School of Computer Science, Beijing Institute of Technology, Beijing
| | - Funmilayo Tade
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - David M. Schuster
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
- Winship Cancer Institute of Emory University, Atlanta, GA
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
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19
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Shi Y, Gao Y, Liao S, Zhang D, Gao Y, Shen D. A Learning-Based CT Prostate Segmentation Method via Joint Transductive Feature Selection and Regression. Neurocomputing 2016; 173:317-331. [PMID: 26752809 PMCID: PMC4704800 DOI: 10.1016/j.neucom.2014.11.098] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
In1 recent years, there has been a great interest in prostate segmentation, which is a important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician's simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms: tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice.
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Affiliation(s)
- Yinghuan Shi
- State Key Laboratory for Novel Software Technology, Nanjing University, China; Department of Radiology and BRIC, UNC Chapel Hill, U.S
| | - Yaozong Gao
- Department of Radiology and BRIC, UNC Chapel Hill, U.S
| | - Shu Liao
- Department of Radiology and BRIC, UNC Chapel Hill, U.S
| | | | - Yang Gao
- State Key Laboratory for Novel Software Technology, Nanjing University, China
| | - Dinggang Shen
- Department of Radiology and BRIC, UNC Chapel Hill, U.S
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20
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Shi Y, Gao Y, Liao S, Zhang D, Gao Y, Shen D. Semi-automatic segmentation of prostate in CT images via coupled feature representation and spatial-constrained transductive lasso. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015; 37:2286-2303. [PMID: 26440268 DOI: 10.1109/tpami.2015.2424869] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Conventional learning-based methods for segmenting prostate in CT images ignore the relations among the low-level features by assuming all these features are independent. Also, their feature selection steps usually neglect the image appearance changes in different local regions of CT images. To this end, we present a novel semi-automatic learning-based prostate segmentation method in this article. For segmenting the prostate in a certain treatment image, the radiation oncologist will be first asked to take a few seconds to manually specify the first and last slices of the prostate. Then, prostate is segmented with the following two steps: (i) Estimation of 3D prostate-likelihood map to predict the likelihood of each voxel being prostate by employing the coupled feature representation, and the proposed Spatial-COnstrained Transductive LassO (SCOTO); (ii) Multi-atlases based label fusion to generate the final segmentation result by using the prostate shape information obtained from both planning and previous treatment images. The major contribution of the proposed method mainly includes: (i) incorporating radiation oncologist's manual specification to aid segmentation, (ii) adopting coupled features to relax previous assumption of feature independency for voxel representation, and (iii) developing SCOTO for joint feature selection across different local regions. The experimental result shows that the proposed method outperforms the state-of-the-art methods in a real-world prostate CT dataset, consisting of 24 patients with totally 330 images, all of which were manually delineated by the radiation oncologist for performance evaluation. Moreover, our method is also clinically feasible, since the segmentation performance can be improved by just requiring the radiation oncologist to spend only a few seconds for manual specification of ending slices in the current treatment CT image.
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21
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Song Y, Cai W, Huang H, Zhou Y, Wang Y, Feng DD. Locality-constrained Subcluster Representation Ensemble for lung image classification. Med Image Anal 2015; 22:102-13. [PMID: 25839422 DOI: 10.1016/j.media.2015.03.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Revised: 03/06/2015] [Accepted: 03/13/2015] [Indexed: 11/30/2022]
Abstract
In this paper, we propose a new Locality-constrained Subcluster Representation Ensemble (LSRE) model, to classify high-resolution computed tomography (HRCT) images of interstitial lung diseases (ILDs). Medical images normally exhibit large intra-class variation and inter-class ambiguity in the feature space. Modelling of feature space separation between different classes is thus problematic and this affects the classification performance. Our LSRE model tackles this issue in an ensemble classification construct. The image set is first partitioned into subclusters based on spectral clustering with approximation-based affinity matrix. Basis representations of the test image are then generated with sparse approximation from the subclusters. These basis representations are finally fused with approximation- and distribution-based weights to classify the test image. Our experimental results on a large HRCT database show good performance improvement over existing popular classifiers.
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Affiliation(s)
- Yang Song
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia.
| | - Weidong Cai
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia
| | - Heng Huang
- Department of Computer Science and Engineering, University of Texas, Arlington, TX 76019, USA
| | - Yun Zhou
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - David Dagan Feng
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia
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22
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Huang M, Yang W, Wu Y, Jiang J, Chen W, Feng Q. Brain Tumor Segmentation Based on Local Independent Projection-Based Classification. IEEE Trans Biomed Eng 2014; 61:2633-45. [DOI: 10.1109/tbme.2014.2325410] [Citation(s) in RCA: 120] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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