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Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis. SENSORS 2021; 21:s21124126. [PMID: 34208548 PMCID: PMC8235629 DOI: 10.3390/s21124126] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/04/2021] [Accepted: 06/10/2021] [Indexed: 12/15/2022]
Abstract
Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH).
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Hegde N, Shishir M, Shashank S, Dayananda P, Latte MV. A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT Colonography. Curr Med Imaging 2021; 17:3-15. [PMID: 32294045 DOI: 10.2174/2213335607999200415141427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 02/09/2020] [Accepted: 02/27/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over the time, they can evolve into colon cancer, which, when diagnosed in later stages, is often fatal. Hence, time is of the essence in the early detection of polyps and the prevention of colon cancer. METHODS To aid this endeavor, many computer-aided methods have been developed, which use a wide array of techniques to detect, localize and segment polyps from CT Colonography images. In this paper, a comprehensive state-of-the-art method is proposed and categorize this work broadly using the available classification techniques using Machine Learning and Deep Learning. CONCLUSION The performance of each of the proposed approach is analyzed with existing methods and also how they can be used to tackle the timely and accurate detection of colon polyps.
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Affiliation(s)
- Niharika Hegde
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
| | - M Shishir
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
| | - S Shashank
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
| | - P Dayananda
- JSS Academy of Technical Education, Bangalore-560060, Karnataka, India
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A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography. Int J Comput Assist Radiol Surg 2020; 16:81-89. [PMID: 33150471 PMCID: PMC7822776 DOI: 10.1007/s11548-020-02275-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/30/2020] [Indexed: 01/08/2023]
Abstract
Purpose Deep learning can be used for improving the performance of computer-aided detection (CADe) in various medical imaging tasks. However, in computed tomographic (CT) colonography, the performance is limited by the relatively small size and the variety of the available training datasets. Our purpose in this study was to develop and evaluate a flow-based generative model for performing 3D data augmentation of colorectal polyps for effective training of deep learning in CADe for CT colonography. Methods We developed a 3D-convolutional neural network (3D CNN) based on a flow-based generative model (3D Glow) for generating synthetic volumes of interest (VOIs) that has characteristics similar to those of the VOIs of its training dataset. The 3D Glow was trained to generate synthetic VOIs of polyps by use of our clinical CT colonography case collection. The evaluation was performed by use of a human observer study with three observers and by use of a CADe-based polyp classification study with a 3D DenseNet. Results The area-under-the-curve values of the receiver operating characteristic analysis of the three observers were not statistically significantly different in distinguishing between real polyps and synthetic polyps. When trained with data augmentation by 3D Glow, the 3D DenseNet yielded a statistically significantly higher polyp classification performance than when it was trained with alternative augmentation methods. Conclusion The 3D Glow-generated synthetic polyps are visually indistinguishable from real colorectal polyps. Their application to data augmentation can substantially improve the performance of 3D CNNs in CADe for CT colonography. Thus, 3D Glow is a promising method for improving the performance of deep learning in CADe for CT colonography.
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Tan J, Gao Y, Liang Z, Cao W, Pomeroy MJ, Huo Y, Li L, Barish MA, Abbasi AF, Pickhardt PJ. 3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2013-2024. [PMID: 31899419 PMCID: PMC7269812 DOI: 10.1109/tmi.2019.2963177] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer. Convolution neural network (CNN) has shown great potential in recognizing different objects (e.g. human faces) from multiple slice (or color) images, a task similar to the polyp differentiation, given a large learning database. This study explores the potential of CNN learning from multiple slice (or feature) images to differentiate malignant from benign polyps from a relatively small database with pathological ground truth, including 32 malignant and 31 benign polyps represented by volumetric computed tomographic (CT) images. The feature image in this investigation is the gray-level co-occurrence matrix (GLCM). For each volumetric polyp, there are 13 GLCMs, computed from each of the 13 directions through the polyp volume. For comparison purpose, the CNN learning is also applied to the multi-slice CT images of the volumetric polyps. The comparison study is further extended to include Random Forest (RF) classification of the Haralick texture features (derived from the GLCMs). From the relatively small database, this study achieved scores of 0.91/0.93 (two-fold/leave-one-out evaluations) AUC (area under curve of the receiver operating characteristics) by using the CNN on the GLCMs, while the RF reached 0.84/0.86 AUC on the Haralick features and the CNN rendered 0.79/0.80 AUC on the multiple-slice CT images. The presented CNN learning from the GLCMs can relieve the challenge associated with relatively small database, improve the classification performance over the CNN on the raw CT images and the RF on the Haralick features, and have the potential to perform the clinical task of differentiating malignant from benign polyps with pathological ground truth.
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Ren Y, Ma J, Xiong J, Chen Y, Lu L, Zhao J. Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography. IEEE J Biomed Health Inform 2019; 23:324-333. [DOI: 10.1109/jbhi.2018.2808199] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Yang S, Han X, Chen Y. Three-Dimensional Embryonic Image Segmentation and Registration Based on Shape Index and Ellipsoid-Fitting Method. J Comput Biol 2018; 26:128-142. [PMID: 30526025 DOI: 10.1089/cmb.2018.0165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Quantitative analysis based on three-dimensional differential interference contrast (DIC) images is currently the mainstream in analyzing gene functions involved in early cell fate specifications. Segmentation and registration are the two most important steps in analysis. Many image segmentation methods have poor performance on embryonic DIC images because of the interference of egg shells, blurs, and nonuniform intensity background. A novel segmentation method is presented based on the shape index (SI) of local intensity variation in DIC images. To compute the SI, the intensity values of a local neighborhood are regarded as z coordinates over x-y planes. After calculating the SI for each pixel by evaluating the shape of intensity surface over the corresponding local neighborhood, SI thresholding is used to detect cytoplasm granules within embryonic boundaries. As a scalar and rotation invariant, the SI is both insensitive to directional changes and different ranges of intensity variations. Embryonic registration methods are usually based on the orientation of vertebrate anteroposterior (AP) axes computed from segmented embryonic boundaries. Due to the blur of marginal slices in DIC images, usually the segmented boundaries are incomplete, which may make the computed AP axes shift away from the correct orientation when using the principal component analysis method. A method calculating AP axes based on ellipsoid-fitting is proposed, which can achieve high accuracy when handling incomplete segmented boundaries. Using Worm Developmental Dynamics Database, we evaluated the performance of the proposed segmentation method and the computation of AP axes. Experimental results show that the two methods outperform existing methods.
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Affiliation(s)
- Sihai Yang
- 1 College of Computer Science and Technology, Huaqiao University , Xiamen, China .,2 Graduate School of Information Science and Engineering, Ritsumeikan University , Kusatsu, Japan
| | - Xianhua Han
- 3 Faculty of Science, Yamaguchi University , Yamaguchi, Japan
| | - Yenwei Chen
- 2 Graduate School of Information Science and Engineering, Ritsumeikan University , Kusatsu, Japan
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A Comparative Study of Modern Machine Learning Approaches for Focal Lesion Detection and Classification in Medical Images: BoVW, CNN and MTANN. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2018. [DOI: 10.1007/978-3-319-68843-5_2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Blanc-Durand P, Van Der Gucht A, Guedj E, Abulizi M, Aoun-Sebaiti M, Lerman L, Verger A, Authier FJ, Itti E. Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach. PLoS One 2017; 12:e0181152. [PMID: 28704562 PMCID: PMC5509294 DOI: 10.1371/journal.pone.0181152] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 06/27/2017] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF 18F-FDG brain profiles. METHODS 18F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated. RESULTS The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%. CONCLUSION We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using 18F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.
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Affiliation(s)
- Paul Blanc-Durand
- Department of Nuclear Medicine, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris/Paris-Est University, Créteil, France
| | - Axel Van Der Gucht
- Department of Nuclear Medicine, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris/Paris-Est University, Créteil, France
| | - Eric Guedj
- Department of Nuclear Medicine, La Timone Hospital, Assistance Publique-Hôpitaux de Marseille, Marseille, France
- Aix-Marseille University, INT, CNRS UMR 7289, Marseille, France
- Aix-Marseille University, CERIMED, Marseille, France
| | - Mukedaisi Abulizi
- Department of Nuclear Medicine, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris/Paris-Est University, Créteil, France
| | - Mehdi Aoun-Sebaiti
- INSERM U955-Team 10, Créteil, France
- Department of Neurology, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris/Paris-Est University, Créteil, France
| | - Lionel Lerman
- Department of Nuclear Medicine, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris/Paris-Est University, Créteil, France
| | - Antoine Verger
- CHU Nancy, Nuclear Medecine & Nancyclotep Experimental Imaging Platform, Nancy, France
| | - François-Jérôme Authier
- INSERM U955-Team 10, Créteil, France
- Department of Pathology, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris/Paris-Est University, Créteil, France
- Reference Center for Neuromuscular Disorders, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris, Créteil, France
| | - Emmanuel Itti
- Department of Nuclear Medicine, H. Mondor Hospital, Assistance Publique-Hôpitaux de Paris/Paris-Est University, Créteil, France
- INSERM U955-GRC Amyloid Research Institute, Créteil, France
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Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol 2017; 10:257-273. [PMID: 28689314 DOI: 10.1007/s12194-017-0406-5] [Citation(s) in RCA: 381] [Impact Index Per Article: 54.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 06/29/2017] [Indexed: 02/07/2023]
Abstract
The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.
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Affiliation(s)
- Kenji Suzuki
- Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3440 South Dearborn Street, Chicago, IL, 60616, USA. .,World Research Hub Initiative (WRHI), Tokyo Institute of Technology, Tokyo, Japan.
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Takahashi R, Kajikawa Y. Computer-aided diagnosis: A survey with bibliometric analysis. Int J Med Inform 2017; 101:58-67. [DOI: 10.1016/j.ijmedinf.2017.02.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 01/28/2017] [Accepted: 02/04/2017] [Indexed: 12/18/2022]
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Cao P, Liu X, Zhang J, Li W, Zhao D, Huang M, Zaiane O. A ℓ 2, 1 norm regularized multi-kernel learning for false positive reduction in Lung nodule CAD. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 140:211-231. [PMID: 28254078 DOI: 10.1016/j.cmpb.2016.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 11/25/2016] [Accepted: 12/12/2016] [Indexed: 06/06/2023]
Abstract
OBJECTIVE The aim of this paper is to describe a novel algorithm for False Positive Reduction in lung nodule Computer Aided Detection(CAD). METHODS In this paper, we describes a new CT lung CAD method which aims to detect solid nodules. Specially, we proposed a multi-kernel classifier with a ℓ2, 1 norm regularizer for heterogeneous feature fusion and selection from the feature subset level, and designed two efficient strategies to optimize the parameters of kernel weights in non-smooth ℓ2, 1 regularized multiple kernel learning algorithm. The first optimization algorithm adapts a proximal gradient method for solving the ℓ2, 1 norm of kernel weights, and use an accelerated method based on FISTA; the second one employs an iterative scheme based on an approximate gradient descent method. RESULTS The results demonstrates that the FISTA-style accelerated proximal descent method is efficient for the ℓ2, 1 norm formulation of multiple kernel learning with the theoretical guarantee of the convergence rate. Moreover, the experimental results demonstrate the effectiveness of the proposed methods in terms of Geometric mean (G-mean) and Area under the ROC curve (AUC), and significantly outperforms the competing methods. CONCLUSIONS The proposed approach exhibits some remarkable advantages both in heterogeneous feature subsets fusion and classification phases. Compared with the fusion strategies of feature-level and decision level, the proposed ℓ2, 1 norm multi-kernel learning algorithm is able to accurately fuse the complementary and heterogeneous feature sets, and automatically prune the irrelevant and redundant feature subsets to form a more discriminative feature set, leading a promising classification performance. Moreover, the proposed algorithm consistently outperforms the comparable classification approaches in the literature.
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Affiliation(s)
- Peng Cao
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
| | - Xiaoli Liu
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
| | - Jian Zhang
- School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China
| | - Wei Li
- Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
| | - Dazhe Zhao
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China
| | - Min Huang
- Information Science and Engineering, Northeastern University, Shenyang, China
| | - Osmar Zaiane
- Computing Science, University of Alberta, Edmonton, Alberta, Canada
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Enhanced 3D segmentation techniques for reconstructed 3D medical volumes: Robust and Accurate Intelligent System. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.procs.2017.08.318] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Ren Y, Ma J, Xiong J, Lu L, Zhao J. High-Performance CAD-CTC Scheme Using Shape Index, Multiscale Enhancement Filters, and Radiomic Features. IEEE Trans Biomed Eng 2016; 64:1924-1934. [PMID: 27893377 DOI: 10.1109/tbme.2016.2631245] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Computer-aided detection (CAD) systems for computed tomography colonography (CTC) can automatically detect colorectal polyps. The main problem of currently developed CAD-CTC systems is the numerous false positives (FPs) caused by the existence of complicated colon structures (e.g., haustral fold, residual fecal material, inflation tube, and ileocecal valve). This study proposes a CAD-CTC scheme using shape index, multiscale enhancement filters, and radiomic features to address the FP issue. METHODS Shape index and multiscale enhancement filter calculated in the Gaussian smoothed geodesic distance field are combined to generate the polyp candidates. A total of 440 well-defined radiomic features collected from previous radiomic studies and 200 newly developed radiomic features are used to construct a supervised classification model to reduce the numerous FPs. RESULTS The proposed CAD-CTC scheme was evaluated on 152 oral contrast-enhanced CT datasets from 76 patients with 103 polyps ≥5 mm. The detection results were 98.1% and 95.3% by-polyp sensitivity and per-scan sensitivity, respectively, with the same FP rate of 1.3 FPs per dataset for polyps ≥5 mm. CONCLUSION Experimental results indicate that the proposed CAD-CTC scheme can achieve high sensitivity while maintaining a low FP rate. SIGNIFICANCE The proposed CAD-CTC scheme would be a beneficial tool in clinical colon examination.
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Abstract
OBJECTIVE Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically. CONCLUSION This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.
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Epstein ML, Obara PR, Chen Y, Liu J, Zarshenas A, Makkinejad N, Dachman AH, Suzuki K. Quantitative radiology: automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing. Quant Imaging Med Surg 2015; 5:673-84. [PMID: 26682137 DOI: 10.3978/j.issn.2223-4292.2015.10.06] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. Our purpose was to develop a computerized measurement of polyp volume in computed tomography colonography (CTC). METHODS We developed a 3D automated scheme for measuring polyp volume at CTC. Our scheme consisted of segmentation of colon wall to confine polyp segmentation to the colon wall, extraction of a highly polyp-like seed region based on the Hessian matrix, a 3D volume growing technique under the minimum surface expansion criterion for segmentation of polyps, and sub-voxel refinement and surface smoothing for obtaining a smooth polyp surface. Our database consisted of 30 polyp views (15 polyps) in CTC scans from 13 patients. Each patient was scanned in the supine and prone positions. Polyp sizes measured in optical colonoscopy (OC) ranged from 6-18 mm with a mean of 10 mm. A radiologist outlined polyps in each slice and calculated volumes by summation of volumes in each slice. The measurement study was repeated 3 times at least 1 week apart for minimizing a memory effect bias. We used the mean volume of the three studies as "gold standard". RESULTS Our measurement scheme yielded a mean polyp volume of 0.38 cc (range, 0.15-1.24 cc), whereas a mean "gold standard" manual volume was 0.40 cc (range, 0.15-1.08 cc). The "gold-standard" manual and computer volumetric reached excellent agreement (intra-class correlation coefficient =0.80), with no statistically significant difference [P (F≤f) =0.42]. CONCLUSIONS We developed an automated scheme for measuring polyp volume at CTC based on Hessian matrix-based shape extraction and volume growing. Polyp volumes obtained by our automated scheme agreed excellently with "gold standard" manual volumes. Our fully automated scheme can efficiently provide accurate polyp volumes for radiologists; thus, it would help radiologists improve the accuracy and efficiency of polyp volume measurements in CTC.
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Affiliation(s)
- Mark L Epstein
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Piotr R Obara
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Yisong Chen
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Junchi Liu
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Amin Zarshenas
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Nazanin Makkinejad
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Abraham H Dachman
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
| | - Kenji Suzuki
- 1 Department of Radiology, The University of Chicago, Chicago, IL, USA ; 2 Department of Radiology, University of New Mexico, Albuquerque, NM, USA ; 3 Department of Radiology, Loyola University Medical Center, Maywood, IL, USA ; 4 Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA ; 5 School of Electronics Engineering and Computer Science, Beijing University, Beijing 100871, China
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Wang H, Liang Z, Li LC, Han H, Song B, Pickhardt PJ, Barish MA, Lascarides CE. An adaptive paradigm for computer-aided detection of colonic polyps. Phys Med Biol 2015; 60:7207-28. [PMID: 26348125 PMCID: PMC4565750 DOI: 10.1088/0031-9155/60/18/7207] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Most previous efforts in developing computer-aided detection (CADe) of colonic polyps apply similar measures or parameters to detect polyps regardless of their locations under an implicit assumption that all the polyps reside in a similar local environment, e.g. on a relatively flat colon wall. In reality, this implicit assumption is frequently invalid, because the haustral folds can have a very different local environment from that of the relatively flat colon wall. We conjecture that this assumption may be a major cause of missing the detection of polyps, especially small polyps (<10 mm linear size) located on the haustral folds. In this paper, we take the concept of adaptiveness and present an adaptive paradigm for CADe of colonic polyps. Firstly, we decompose the complicated colon structure into two simplified sub-structures, each of which has similar properties, of (1) relatively flat colon wall and (2) ridge-shaped haustral folds. Then we develop local environment descriptions to adaptively reflect each of these two simplified sub-structures. To show the impact of the adaptiveness of the local environment descriptions upon the polyp detection task, we focus on the local geometrical measures of the volume data for both the detection of initial polyp candidates (IPCs) and the reduction of false positives (FPs) in the IPC pool. The experimental outcome using the local geometrical measures is very impressive such that not only the previously-missed small polyps on the folds are detected, but also the previously miss-removed small polyps on the folds during FP reduction are retained. It is expected that this adaptive paradigm will have a great impact on detecting the small polyps, measuring their volumes and volume changes over time, and optimizing their management plan.
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Affiliation(s)
- Huafeng Wang
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
- School of Software, Beihang Univ., Beijing 10083, China
| | - Zhengrong Liang
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
| | - Lihong C. Li
- Dept. of Engineering Science & Physics, City Univ. of New York, Staten Island, NY 10314, USA
| | - Hao Han
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
| | - Bowen Song
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
| | - Perry J. Pickhardt
- Dept. of Radiology, Univ. of Wisconsin Medical School, Madison, WI 53792, USA
| | - Matthew A. Barish
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
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Saha PK, Strand R, Borgefors G. Digital Topology and Geometry in Medical Imaging: A Survey. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1940-1964. [PMID: 25879908 DOI: 10.1109/tmi.2015.2417112] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.
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Motai Y, Ma D, Docef A, Yoshida H. Smart Colonography for Distributed Medical Databases with Group Kernel Feature Analysis. ACM T INTEL SYST TEC 2015. [DOI: 10.1145/2668136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Computer-Aided Detection (CAD) of polyps in Computed Tomographic (CT) colonography is currently very limited since a single database at each hospital/institution doesn't provide sufficient data for training the CAD system's classification algorithm. To address this limitation, we propose to use multiple databases, (e.g., big data studies) to create multiple institution-wide databases using distributed computing technologies, which we call smart colonography. Smart colonography may be built by a larger colonography database networked through the participation of multiple institutions via distributed computing. The motivation herein is to create a distributed database that increases the detection accuracy of CAD diagnosis by covering many true-positive cases. Colonography data analysis is mutually accessible to increase the availability of resources so that the knowledge of radiologists is enhanced. In this article, we propose a scalable and efficient algorithm called Group Kernel Feature Analysis (GKFA), which can be applied to multiple cancer databases so that the overall performance of CAD is improved. The key idea behind the proposed GKFA method is to allow the feature space to be updated as the training proceeds with more data being fed from other institutions into the algorithm. Experimental results show that GKFA achieves very good classification accuracy.
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Affiliation(s)
| | | | - Alen Docef
- Virginia Commonwealth University, VA, USA
| | - Hiroyuki Yoshida
- Massachusetts General Hospital and Harvard Medical School, MA, USA
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20
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Näppi JJ, Regge D, Yoshida H. Context-specific method for detection of soft-tissue lesions in non-cathartic low-dose dual-energy CT colonography. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9414:94142Y. [PMID: 25964710 DOI: 10.1117/12.2081284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
In computed tomographic colonography (CTC), orally administered fecal-tagging agents can be used to indicate residual feces and fluid that could otherwise hide or imitate lesions on CTC images of the colon. Although the use of fecal tagging improves the detection accuracy of CTC, it can introduce image artifacts that may cause lesions that are covered by fecal tagging to have a different visual appearance than those not covered by fecal tagging. This can distort the values of image-based computational features, thereby reducing the accuracy of computer-aided detection (CADe). We developed a context-specific method that performs the detection of lesions separately on lumen regions covered by air and on those covered by fecal tagging, thereby facilitating the optimization of detection parameters separately for these regions and their detected lesion candidates to improve the detection accuracy of CADe. For pilot evaluation, the method was integrated into a dual-energy CADe (DE-CADe) scheme and evaluated by use of leave-one-patient-out evaluation on 66 clinical non-cathartic low-dose dual-energy CTC (DE-CTC) cases that were acquired at a low effective radiation dose and reconstructed by use of iterative image reconstruction. There were 22 colonoscopy-confirmed lesions ≥6 mm in size in 21 patients. The DE-CADe scheme detected 96% of the lesions at a median of 6 FP detections per patient. These preliminary results indicate that the use of context-specific detection can yield high detection accuracy of CADe in non-cathartic low-dose DE-CTC examinations.
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Affiliation(s)
- Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA 02114, USA
| | - Daniele Regge
- Institute for Cancer Research and Treatment, Strada Provinciale 142, IT-10060 Candiolo, Turin, Italy
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA 02114, USA
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Yang X, Ye X, Slabaugh G. Multilabel Region Classification and Semantic Linking for Colon Segmentation in CT Colonography. IEEE Trans Biomed Eng 2015; 62:948-59. [DOI: 10.1109/tbme.2014.2374355] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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22
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Nasirudin RA, Tachibana R, Näppi JJ, Mei K, Kopp FK, Rummeny EJ, Yoshida H, Noël PB. A comparison of material decomposition techniques for dual-energy CT colonography. ACTA ACUST UNITED AC 2015; 9412. [PMID: 25918480 DOI: 10.1117/12.2081982] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
In recent years, dual-energy computed tomography (DECT) has been widely used in the clinical routine due to improved diagnostics capability from additional spectral information. One promising application for DECT is CT colonography (CTC) in combination with computer-aided diagnosis (CAD) for detection of lesions and polyps. While CAD has demonstrated in the past that it is able to detect small polyps, its performance is highly dependent on the quality of the input data. The presence of artifacts such as beam-hardening and noise in ultra-low-dose CTC may severely degrade detection performances of small polyps. In this work, we investigate and compare virtual monochromatic images, generated by image-based decomposition and projection-based decomposition, with respect to CAD performance. In the image-based method, reconstructed images are firstly decomposed into water and iodine before the virtual monochromatic images are calculated. On the contrary, in the projection-based method, the projection data are first decomposed before calculation of virtual monochromatic projection and reconstruction. Both material decomposition methods are evaluated with regards to the accuracy of iodine detection. Further, the performance of the virtual monochromatic images is qualitatively and quantitatively assessed. Preliminary results show that the projection-based method does not only have a more accurate detection of iodine, but also delivers virtual monochromatic images with reduced beam hardening artifacts in comparison with the image-based method. With regards to the CAD performance, the projection-based method yields an improved detection performance of polyps in comparison with that of the image-based method.
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Affiliation(s)
- Radin A Nasirudin
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
| | - Rie Tachibana
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kai Mei
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
| | - Felix K Kopp
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
| | - Ernst J Rummeny
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter B Noël
- Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany
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Ischemic stroke detection system with a computer-aided diagnostic ability using an unsupervised feature perception enhancement method. Int J Biomed Imaging 2014; 2014:947539. [PMID: 25610453 PMCID: PMC4276348 DOI: 10.1155/2014/947539] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 11/02/2014] [Accepted: 11/11/2014] [Indexed: 11/18/2022] Open
Abstract
We propose an ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method. In the first step, known as preprocessing, we use a cubic curve contrast enhancement method to enhance image contrast. In the second step, we use a series of methods to extract the brain tissue image area identified during preprocessing. To detect abnormal regions in the brain images, we propose using an unsupervised region growing algorithm to segment the brain tissue area. The brain is centered on a horizontal line and the white matter of the brain's inner ring is split into eight regions. In the third step, we use a coinciding regional location method to find the hybrid area of locations where a stroke may have occurred in each cerebral hemisphere. Finally, we make corrections and mark the stroke area with red color. In the experiment, we tested the system on 90 computed tomography (CT) images from 26 patients, and, with the assistance of two radiologists, we proved that our proposed system has computer-aided diagnostic capabilities. Our results show an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images.
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Lu L, Zhao J. Virtual colon flattening method based on colonic outer surface. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:473-481. [PMID: 25443576 DOI: 10.1016/j.cmpb.2014.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Revised: 10/02/2014] [Accepted: 10/07/2014] [Indexed: 06/04/2023]
Abstract
Virtual colon flattening (VF) is a minimally invasive viewing mode used to detect colorectal polyps on the colonic inner surface in virtual colonoscopy. Compared with conventional colonoscopy, inspecting a flattened colonic inner surface is faster and results in fewer uninspected regions. Unfortunately, the deformation distortions of flattened colonic inner surface impede the performance of VF. Conventionally, the deformation distortions can be corrected by using the colonic inner surface. However, colonic curvatures and haustral folds make correcting deformation distortions using only the colonic inner surface difficult. Therefore, we propose a VF method that is based on the colonic outer surface. The proposed method includes two novel algorithms, namely, the colonic outer surface extraction algorithm and the colonic outer surface-based distortion correction algorithm. Sixty scans involving 77 annotated polyps were used for the validation. The flattened colons were independently inspected by three operators and then compared with three existing VF methods. The correct detection rates of the proposed method and the three existing methods were 79.6%, 67.1%, 71.9%, and 72.7%, respectively, and the false positives per scan were 0.16, 0.32, 0.21, and 0.26, respectively. The experimental results demonstrate that our proposed method has better performance than existing methods that are based on the colonic inner surface.
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Affiliation(s)
- Lin Lu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography. Int J Comput Assist Radiol Surg 2014; 9:79-89. [PMID: 23797823 DOI: 10.1007/s11548-013-0913-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2012] [Accepted: 06/10/2013] [Indexed: 02/05/2023]
Abstract
PURPOSE Computer-aided detection and diagnosis (CAD) of colonic polyps always faces the challenge of classifying imbalanced data. In this paper, three new operating point selection strategies based on receiver operating characteristic curve are proposed to address the problem. METHODS Classification on imbalanced data performs inferiorly because of a major reason that the best differentiation threshold shifts due to the degree of data imbalance. To address this decision threshold shifting issue, three operating point selection strategies, i.e., shortest distance, harmonic mean and anti-harmonic mean, are proposed and their performances are investigated. RESULTS Experiments were conducted on a class-imbalanced database, which contains 64 polyps in 786 polyp candidates. Support vector machine (SVM) and random forests (RFs) were employed as basic classifiers. Two imbalanced data correcting techniques, i.e., cost-sensitive learning and training data down sampling, were applied to SVM and RFs, and their performances were compared with the proposed strategies. Comparing to the original thresholding method, i.e., 0.488 sensitivity and 0.986 specificity for RFs and 0.526 sensitivity and 0.977 specificity for SVM, our strategies achieved more balanced results, which are around 0.89 sensitivity and 0.92 specificity for RFs and 0.88 sensitivity and 0.90 specificity for SVM. Meanwhile, their performance remained at the same level regardless of whether other correcting methods are used. CONCLUSIONS Based on the above experiments, the gain of our proposed strategies is noticeable: the sensitivity improved from 0.5 to around 0.88 for RFs and 0.89 for SVM while remaining a relatively high level of specificity, i.e., 0.92 for RFs and 0.90 for SVM. The performance of our proposed strategies was adaptive and robust with different levels of imbalanced data. This indicates a feasible solution to the shifting problem for favorable sensitivity and specificity in CAD of polyps from imbalanced data.
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FIORI MARCELO, MUSÉ PABLO, SAPIRO GUILLERMO. A COMPLETE SYSTEM FOR CANDIDATE POLYPS DETECTION IN VIRTUAL COLONOSCOPY. INT J PATTERN RECOGN 2014. [DOI: 10.1142/s0218001414600143] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We present a computer-aided detection pipeline for polyp detection in Computer tomographic colonography. The first stage of the pipeline consists of a simple colon segmentation technique that enhances polyps, which is followed by an adaptive-scale candidate polyp delineation, in order to capture the appropriate polyp size. In the last step, candidates are classified based on new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The system is tested with ground truth data, including flat and small polyps which are hard to detect even with optical colonoscopy. We achieve 100% sensitivity for polyps larger than 6 mm in size with just 0.9 false positives per case, and 93% sensitivity with 2.8 false positives per case for polyps larger than 3 mm in size.
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Affiliation(s)
- MARCELO FIORI
- Facultad de Ingeniería, Universidad de la República, Uruguay
| | - PABLO MUSÉ
- Facultad de Ingeniería, Universidad de la República, Uruguay
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Mamonov AV, Figueiredo IN, Figueiredo PN, Tsai YHR. Automated polyp detection in colon capsule endoscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1488-1502. [PMID: 24710829 DOI: 10.1109/tmi.2014.2314959] [Citation(s) in RCA: 85] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame.We assume that the polyps are characterized as protrusions that are mostly round in shape. Thus, a best fit ball radius is used as a decision parameter of the classifier. We present a statistical performance evaluation of our approach on a data set containing over 18 900 frames from the endoscopic video sequences of five adult patients. The algorithm achieves 47% sensitivity per frame and 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by an operator.
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28
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Song B, Zhang G, Lu H, Wang H, Zhu W, J Pickhardt P, Liang Z. Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography. Int J Comput Assist Radiol Surg 2014; 9:1021-31. [PMID: 24696313 DOI: 10.1007/s11548-014-0991-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2013] [Accepted: 03/06/2014] [Indexed: 02/06/2023]
Abstract
PURPOSE Differentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic lesions, is of fundamental importance for patient management. Image intensity-based textural features have been recognized as useful biomarker for the differentiation task. In this paper, we introduce texture features from higher-order images, i.e., gradient and curvature images, beyond the intensity image, for that task. METHODS Based on the Haralick texture analysis method, we introduce a virtual pathological model to explore the utility of texture features from high-order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on a database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the support vector machine classifier and the merit of area under the curve (AUC) of the receiver operating characteristics. RESULTS The AUC of classification was improved from 0.74 (using the image intensity alone) to 0.85 (by also considering the gradient and curvature images) in differentiating the neoplastic lesions from non-neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas. CONCLUSIONS The experimental results demonstrated that texture features from higher-order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography colonography for colorectal cancer screening by not only detecting polyps but also classifying them for optimal polyp management for the best outcome in personalized medicine.
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Affiliation(s)
- Bowen Song
- Department of Radiology, Stony Brook University, Stony Brook, NY , 11790, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY , 11790, USA
| | - Guopeng Zhang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an , 710032, Shaanxi, China
| | - Hongbing Lu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an , 710032, Shaanxi, China
| | - Huafeng Wang
- Department of Radiology, Stony Brook University, Stony Brook, NY , 11790, USA
| | - Wei Zhu
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY , 11790, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI , 53792, USA
| | - Zhengrong Liang
- Department of Radiology, Stony Brook University, Stony Brook, NY , 11790, USA.
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Cao P, Yang J, Li W, Zhao D, Zaiane O. Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD. Comput Med Imaging Graph 2014; 38:137-50. [DOI: 10.1016/j.compmedimag.2013.12.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2013] [Revised: 10/19/2013] [Accepted: 12/02/2013] [Indexed: 01/15/2023]
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Xu JW, Suzuki K. Max-AUC feature selection in computer-aided detection of polyps in CT colonography. IEEE J Biomed Health Inform 2014; 18:585-93. [PMID: 24608058 PMCID: PMC4283828 DOI: 10.1109/jbhi.2013.2278023] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.
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Affiliation(s)
- Jian-Wu Xu
- Department of Radiology, University of Chicago, Chicago, IL 60637 USA
| | - Kenji Suzuki
- Department of Radiology, University of Chicago, Chicago, IL 60637 USA
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Feasibility of using the marginal blood vessels as reference landmarks for CT colonography. AJR Am J Roentgenol 2014; 202:W50-8. [PMID: 24370165 DOI: 10.2214/ajr.12.10463] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE The purpose of this study was to show the spatial relationship of the colonic marginal blood vessels and the teniae coli on CT colonography (CTC) and the use of the marginal blood vessels for supine-prone registration of polyps and for determination of proper connectivity of collapsed colonic segments. MATERIALS AND METHODS We manually labeled the marginal blood vessels on 15 CTC examinations. Colon segmentation, centerline extraction, teniae detection, and teniae identification were automatically performed. For assessment of their spatial relationships, the distances from the marginal blood vessels to the three teniae coli and to the colon were measured. Student t tests (paired, two-tailed) were performed to evaluate the differences among these distances. To evaluate the reliability of the marginal vessels as reference points for polyp correlation, we analyzed 20 polyps from 20 additional patients who underwent supine and prone CTC. The average difference of the circumferential polyp position on the supine and prone scans was computed. Student t tests (paired, two-tailed) were performed to evaluate the supine-prone differences of the distance. We performed a study on 10 CTC studies from 10 patients with collapsed colonic segments by manually tracing the marginal blood vessels near the collapsed regions to resolve the ambiguity of the colon path. RESULTS The average distances (± SD) from the marginal blood vessels to the tenia mesocolica, tenia omentalis, and tenia libera were 20.1 ± 3.1 mm (95% CI, 18.5-21.6 mm), 39.5 ± 4.8 mm (37.1-42.0 mm), and 36.9 ± 4.2 mm (34.8-39.1 mm), respectively. Pairwise comparison showed that these distances to the tenia libera and tenia omentalis were significantly different from the distance to the tenia mesocolica (p < 0.001). The average distance from the marginal blood vessels to the colon wall was 15.3 ± 2.0 mm (14.2-16.3 mm). For polyp localization, the average difference of the circumferential polyp position on the supine and prone scans was 9.6 ± 9.4 mm (5.5-13.7 mm) (p = 0.15) and expressed as a percentage of the colon circumference was 3.1% ± 2.0% (2.3-4.0%) (p = 0.83). We were able to trace the marginal blood vessels for 10 collapsed colonic segments and determine the paths of the colon in these regions. CONCLUSION The marginal blood vessels run parallel to the colon in proximity to the tenia mesocolica and enable accurate supine-prone registration of polyps and localization of the colon path in areas of collapse. Thus, the marginal blood vessels may be used as reference landmarks complementary to the colon centerline and teniae coli.
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Automatic rectum limit detection by anatomical markers correlation. Comput Med Imaging Graph 2014; 38:245-50. [PMID: 24598410 DOI: 10.1016/j.compmedimag.2014.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 12/19/2013] [Accepted: 01/23/2014] [Indexed: 12/27/2022]
Abstract
Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems. In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems.
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Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2014. [DOI: 10.1007/978-3-642-40017-9_5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Parot V, Lim D, González G, Traverso G, Nishioka NS, Vakoc BJ, Durr NJ. Photometric stereo endoscopy. JOURNAL OF BIOMEDICAL OPTICS 2013; 18:076017. [PMID: 23864015 PMCID: PMC4407669 DOI: 10.1117/1.jbo.18.7.076017] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Revised: 06/14/2013] [Accepted: 06/21/2013] [Indexed: 05/18/2023]
Abstract
While color video endoscopy has enabled wide-field examination of the gastrointestinal tract, it often misses or incorrectly classifies lesions. Many of these missed lesions exhibit characteristic three-dimensional surface topographies. An endoscopic system that adds topographical measurements to conventional color imagery could therefore increase lesion detection and improve classification accuracy. We introduce photometric stereo endoscopy (PSE), a technique which allows high spatial frequency components of surface topography to be acquired simultaneously with conventional two-dimensional color imagery. We implement this technique in an endoscopic form factor and demonstrate that it can acquire the topography of small features with complex geometries and heterogeneous optical properties. PSE imaging of ex vivo human gastrointestinal tissue shows that surface topography measurements enable differentiation of abnormal shapes from surrounding normal tissue. Together, these results confirm that the topographical measurements can be obtained with relatively simple hardware in an endoscopic form factor, and suggest the potential of PSE to improve lesion detection and classification in gastrointestinal imaging.
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Affiliation(s)
- Vicente Parot
- Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Wellman Center for Photomedicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Daryl Lim
- Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Wellman Center for Photomedicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Germán González
- Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Wellman Center for Photomedicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Giovanni Traverso
- Harvard Medical School, Massachusetts General Hospital, Division of Gastroenterology, Boston, Massachusetts 02114
- Massachusetts Institute of Technology, Koch Institute for Integrative Cancer Research, Department of Chemical Engineering, Cambridge, Massachusetts 02139
| | - Norman S. Nishioka
- Harvard Medical School, Massachusetts General Hospital, Division of Gastroenterology, Boston, Massachusetts 02114
| | - Benjamin J. Vakoc
- Wellman Center for Photomedicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Nicholas J. Durr
- Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Wellman Center for Photomedicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts 02114
- Address all correspondence to: Nicholas J. Durr, Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139. Tel: 617-324-4227; Fax: 617-643-9208; E-mail:
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Suzuki K. Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013; E96-D:772-783. [PMID: 24174708 PMCID: PMC3810349 DOI: 10.1587/transinf.e96.d.772] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine "optimal" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.
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Affiliation(s)
- Kenji Suzuki
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
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Näppi JJ, Kim SH, Yoshida H. Volumetric detection of colorectal lesions for noncathartic dual-energy computed tomographic colonography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:3740-3. [PMID: 23366741 DOI: 10.1109/embc.2012.6346780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Noncathartic computed tomographic colonography (CTC) could significantly increase patient adherence to colorectal screening guidelines. However, radiologists find the interpretation of noncathartic CTC images challenging. We developed a fully automated computer-aided detection (CAD) scheme for assisting radiologists with noncathartic CTC. A volumetric method is used to detect lesions within a thick target region encompassing the colonic wall. Dual-energy CTC (DE-CTC) is used to provide more detailed information about the colon than what is possible with conventional CTC. False-positive detections are reduced by use of a random-forest classifier. The effect of the thickness of the target region on detection performance was assessed by use of 22 clinical noncathartic DE-CTC studies including 27 lesions ≥6 mm. The results indicate that the thickness parameter can have significant effect on detection accuracy. Leave-one-patient-out evaluation indicated that the proposed CAD scheme detects colorectal lesions at high accuracy in noncathartic CTC.
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Affiliation(s)
- Janne J Näppi
- Department of Radiology of Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA 02114, USA.
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Näppi JJ, Do S, Yoshida H. Computer-Aided Detection of Colorectal Lesions with Super-Resolution CT Colonography: Pilot Evaluation. ABDOMINAL IMAGING : COMPUTATION AND CLINICAL APPLICATIONS : 5TH INTERNATIONAL WORKSHOP, HELD IN CONJUNCTION WITH MICCAI 2013, NAGOYA, JAPAN, SEPTEMBER 22, 2013 : PROCEEDINGS. ABDOMINAL IMAGING (WORKSHOP) (5TH : 2013 : NAGOYA-SHI, JAPAN) 2013; 8198:73-80. [PMID: 25580475 PMCID: PMC4287197 DOI: 10.1007/978-3-642-41083-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Reliable computer-aided detection (CADe) of small polyps and flat lesions is limited by the relatively low image resolution of computed tomographic colonography (CTC). We developed a sinogram-based super-resolution (SR) method to enhance the images of lesion candidates detected by CADe. First, CADe is used to detect lesion candidates at high sensitivity from conventional CTC images. Next, the signal patterns of the lesion candidates are enhanced in sinogram domain by use of non-uniform compressive sampling and iterative reconstruction to produce SR images of the lesion candidates. For pilot evaluation, an anthropomorphic phantom including simulated lesions was filled partially with fecal tagging and scanned by use of a CT scanner. A fully automated CADe scheme was used to detect lesion candidates in the images reconstructed at conventional 0.61-mm and at 0.10-mm SR image resolution. The proof-of-concept results indicate that the SR method has potential to reduce the number of FP CADe detections below that obtainable with the conventional CTC imaging technology.
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Suzuki K. A review of computer-aided diagnosis in thoracic and colonic imaging. Quant Imaging Med Surg 2012; 2:163-76. [PMID: 23256078 DOI: 10.3978/j.issn.2223-4292.2012.09.02] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Accepted: 09/19/2012] [Indexed: 12/24/2022]
Abstract
Medical imaging has been indispensable in medicine since the discovery of x-rays. Medical imaging offers useful information on patients' medical conditions and on the causes of their symptoms and diseases. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must interpret. Thus, computer aids are demanded and become indispensable in physicians' decision making based on medical images. Consequently, computer-aided detection and diagnosis (CAD) has been investigated and has been an active research area in medical imaging. CAD is defined as detection and/or diagnosis made by a radiologist/physician who takes into account the computer output as a "second opinion". In CAD research, detection and diagnosis of lung and colorectal cancer in thoracic and colonic imaging constitute major areas, because lung and colorectal cancers are the leading and second leading causes, respectively, of cancer deaths in the U.S. and also in other countries. In this review, CAD of the thorax and colon, including CAD for detection and diagnosis of lung nodules in thoracic CT, and that for detection of polyps in CT colonography, are reviewed.
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Affiliation(s)
- Kenji Suzuki
- Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
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Zhu H, Barish M, Pickhardt P, Liang Z. Haustral fold segmentation with curvature-guided level set evolution. IEEE Trans Biomed Eng 2012. [PMID: 23193228 DOI: 10.1109/tbme.2012.2226242] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Human colon has complex structures mostly because of the haustral folds. The folds are thin flat protrusions on the colon wall, which complicate the shape analysis for computer-aided detection (CAD) of colonic polyps. Fold segmentation may help reduce the structural complexity, and the folds can serve as an anatomic reference for computed tomographic colonography (CTC). Therefore, in this study, based on a model of the haustral fold boundaries, we developed a level-set approach to automatically segment the fold surfaces. To evaluate the developed fold segmentation algorithm, we first established the ground truth of haustral fold boundaries by experts' drawing on 15 patient CTC datasets without severe under/over colon distention from two medical centers. The segmentation algorithm successfully detected 92.7% of the folds in the ground truth. In addition to the sensitivity measure, we further developed a merit of segmented-area ratio (SAR), i.e., the ratio between the area of the intersection and union of the expert-drawn folds and the area of the automatically segmented folds, to measure the segmentation accuracy. The segmentation algorithm reached an average value of SAR = 86.2%, showing a good match with the ground truth on the fold surfaces. We believe the automatically segmented fold surfaces have the potential to benefit many postprocedures in CTC, such as CAD, taenia coli extraction, supine-prone registration, etc.
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Affiliation(s)
- Hongbin Zhu
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA.
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Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16:933-51. [PMID: 22465077 PMCID: PMC3372692 DOI: 10.1016/j.media.2012.02.005] [Citation(s) in RCA: 322] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2011] [Revised: 01/05/2012] [Accepted: 02/12/2012] [Indexed: 02/06/2023]
Abstract
In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.
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Affiliation(s)
- Shijun Wang
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10 Room 1C224D MSC 1182, Bethesda, MD 20892-1182
| | - Ronald M. Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10 Room 1C224D MSC 1182, Bethesda, MD 20892-1182
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41
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Wei Z, Yao J, Wang S, Liu J, Summers RM. Automated teniae coli detection and identification on computed tomographic colonography. Med Phys 2012; 39:964-75. [PMID: 22320805 DOI: 10.1118/1.3679013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Computed tomographic colonography (CTC) is a minimally invasive technique for colonic polyps and cancer screening. Teniae coli are three bands of longitudinal smooth muscle on the colon surface. Teniae coli are important anatomically meaningful landmarks on human colon. In this paper, the authors propose an automatic teniae coli detection method for CT colonography. METHODS The original CTC slices are first segmented and reconstructed to a 3D colon surface. Then, the 3D colon surface is unfolded using a reversible projection technique. After that the unfolded colon is projected to a 2D height map. The teniae coli are detected using the height map and then reversely projected back to the 3D colon. Since teniae are located at the junctions where the haustral folds meet, the authors apply 2D Gabor filter banks to extract features of haustral folds. The maximum response of the filter banks is then selected as the feature image. The fold centers are then identified based on local maxima and thresholding on the feature image. Connecting the fold centers yields a path of the folds. Teniae coli are extracted as lines running between the fold paths. The authors used the spatial relationship between ileocecal valve (ICV) and teniae mesocolica (TM) to identify the TM, then the teniae omentalis (TO) and the teniae libera (TL) can be identified subsequently. RESULTS The authors tested the proposed method on 47 cases of 37 patients, 10 of the patients with both supine and prone CT scans. The proposed method yielded performance with an average normalized root mean square error (RMSE) ( ± standard deviation [95% confidence interval]) of 4.87% ( ± 2.93%, [4.05% 5.69%]). CONCLUSIONS The proposed fully-automated teniae coli detection and identification method is accurate and promising for future clinical applications.
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Affiliation(s)
- Zhuoshi Wei
- National Institutes of Health Clinical Center, Bethesda, MD 20892-1182, USA
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Wang S, McKenna MT, Nguyen TB, Burns JE, Petrick N, Sahiner B, Summers RM. Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:1141-53. [PMID: 22552333 PMCID: PMC3480731 DOI: 10.1109/tmi.2012.2187304] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.
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Affiliation(s)
- Shijun Wang
- National Institutes of Health, Bethesda, MD, 20892 USA
| | | | - Tan B. Nguyen
- National Institutes of Health, Bethesda, MD, 20892 USA
| | - Joseph E. Burns
- Department of Radiological Sciences, University of California, Irvine, School of Medicine, Orange, CA 92868 USA
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993 USA
| | - Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993 USA
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Pixel-based machine learning in medical imaging. Int J Biomed Imaging 2012; 2012:792079. [PMID: 22481907 PMCID: PMC3299341 DOI: 10.1155/2012/792079] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2011] [Accepted: 11/14/2011] [Indexed: 11/24/2022] Open
Abstract
Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require “learning from examples.” One of the most popular uses of ML is classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates. Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of features calculated from segmented objects as input information; thus, feature calculation or segmentation is not required. Because the PML can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers (i.e., feature-based MLs). In this paper, PMLs are surveyed to make clear (a) classes of PMLs, (b) similarities and differences within (among) different PMLs and those between PMLs and feature-based MLs, (c) advantages and limitations of PMLs, and (d) their applications in medical imaging.
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Faust O, Acharya UR, Tamura T. Formal Design Methods for Reliable Computer-Aided Diagnosis: A Review. IEEE Rev Biomed Eng 2012; 5:15-28. [DOI: 10.1109/rbme.2012.2184750] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Comparative Performance of Random Forest and Support Vector Machine Classifiers for Detection of Colorectal Lesions in CT Colonography. LECTURE NOTES IN COMPUTER SCIENCE 2012. [DOI: 10.1007/978-3-642-28557-8_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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47
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Wei X, Zhu J, Gong H, Xu J, Xu Y. A novel foam fluid negative contrast medium for clear visualization of the colon wall in CT imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2011; 6:465-73. [PMID: 22144024 DOI: 10.1002/cmmi.446] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
| | - Jiong Zhu
- Department of Radiology; Renji Hospital; Shanghai Jiao Tong University; Shanghai; 200127; People's Republic of China
| | - Hongxia Gong
- Department of Radiology; Renji Hospital; Shanghai Jiao Tong University; Shanghai; 200127; People's Republic of China
| | - Jianrong Xu
- Department of Radiology; Renji Hospital; Shanghai Jiao Tong University; Shanghai; 200127; People's Republic of China
| | - Yuhong Xu
- School of Pharmacy; Shanghai Jiao Tong University; Shanghai; 200240; People's Republic of China
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Wang S, Yao J, Petrick N, Summers RM. Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2011; 9:1-15. [PMID: 20953299 DOI: 10.1142/s1469026810002744] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per scan rate of 5, the sensitivity of the SVM using the combined features improved from 0.77 (Group A) and 0.73 (Group B) to 0.83 (p ≤ 0.01).
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Affiliation(s)
- Shijun Wang
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10 Room 1C368X MSC 1182, Bethesda, MD 20892-1182
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Kubota K, Kuroda J, Yoshida M, Ohta K, Kitajima M. Medical image analysis: computer-aided diagnosis of gastric cancer invasion on endoscopic images. Surg Endosc 2011; 26:1485-9. [DOI: 10.1007/s00464-011-2036-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2011] [Accepted: 08/31/2011] [Indexed: 12/18/2022]
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Liu J, Kabadi S, Van Uitert R, Petrick N, Deriche R, Summers RM. Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimation. Med Phys 2011; 38:4276-84. [PMID: 21859029 DOI: 10.1118/1.3596529] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
PURPOSE Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation's effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. METHODS The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. RESULTS Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. CONCLUSIONS The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC.
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Affiliation(s)
- Jiamin Liu
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892-1182, USA
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