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Cross-domain decision making based on criterion weights and risk attitudes for the diagnosis of breast lesions. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10394-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
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2
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Sarkar S, Mali K. Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters. Digit Health 2023; 9:20552076231207203. [PMID: 37860702 PMCID: PMC10583530 DOI: 10.1177/20552076231207203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/26/2023] [Indexed: 10/21/2023] Open
Abstract
Background Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation. Objective The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately. Method In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Results The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision-recall curve. Conclusion Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.
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Affiliation(s)
- Suvobrata Sarkar
- Department of Computer Science and Engineering, Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India
| | - Kalyani Mali
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
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3
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Guo Z, Xie J, Wan Y, Zhang M, Qiao L, Yu J, Chen S, Li B, Yao Y. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Open Life Sci 2022; 17:1600-1611. [PMID: 36561500 PMCID: PMC9743193 DOI: 10.1515/biol-2022-0517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/07/2022] [Accepted: 09/24/2022] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.
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Affiliation(s)
- Zicheng Guo
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Jiping Xie
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Yi Wan
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Min Zhang
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Liang Qiao
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Jiaxuan Yu
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Sijing Chen
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Bingxin Li
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
| | - Yongqiang Yao
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Road, Dalian City, 116001, China
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4
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Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling. Healthcare (Basel) 2022; 10:healthcare10122367. [PMID: 36553891 PMCID: PMC9777990 DOI: 10.3390/healthcare10122367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022] Open
Abstract
Breast cancer is one of the most widely recognized diseases after skin cancer. Though it can occur in all kinds of people, it is undeniably more common in women. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. In this study, artificial intelligence was used to rapidly detect breast cancer by analyzing ultrasound images from the Breast Ultrasound Images Dataset (BUSI), which consists of three categories: Benign, Malignant, and Normal. The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. The proposed framework is discovered to be effective, substantiating outcomes with only raw image evaluation giving a 78.97% test accuracy and masked image evaluation giving 81.02% test precision, which could decrease human errors in the determination cycle. Additionally, our described framework accomplishes higher accuracy after using multi-headed CNN with two processed datasets based on masked and original images, where the accuracy hopped up to 92.31% (±2) with a Mean Squared Error (MSE) loss of 0.05. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. Finally, a web interface has been made to make this model usable for non-technical personals.
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Baughan N, Douglas L, Giger ML. Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening. JOURNAL OF BREAST IMAGING 2022; 4:451-459. [PMID: 38416954 DOI: 10.1093/jbi/wbac052] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Indexed: 03/01/2024]
Abstract
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current state, and future of AI in breast cancer screening and diagnosis along with challenges involved in the development of AI systems. Although AI has been developing for interpretation tasks associated with breast cancer screening for decades, its potential to combat the subjective nature and improve the efficiency of human image interpretation is always expanding. The rapid advancement of computational power and deep learning has increased greatly in AI research, with promising performance in detection and classification tasks across imaging modalities. Most AI systems, based on human-engineered or deep learning methods, serve as concurrent or secondary readers, that is, as aids to radiologists for a specific, well-defined task. In the future, AI may be able to perform multiple integrated tasks, making decisions at the level of or surpassing the ability of humans. Artificial intelligence may also serve as a partial primary reader to streamline ancillary tasks, triaging cases or ruling out obvious normal cases. However, before AI is used as an independent, autonomous reader, various challenges need to be addressed, including explainability and interpretability, in addition to repeatability and generalizability, to ensure that AI will provide a significant clinical benefit to breast cancer screening across all populations.
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Affiliation(s)
- Natalie Baughan
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Lindsay Douglas
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Maryellen L Giger
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
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6
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Ding W, Wang J, Zhou W, Zhou S, Chang C, Shi J. Joint Localization and Classification of Breast Cancer in B-Mode Ultrasound Imaging via Collaborative Learning with Elastography. IEEE J Biomed Health Inform 2022; 26:4474-4485. [PMID: 35763467 DOI: 10.1109/jbhi.2022.3186933] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performing them simultaneously. Besides, they suffer from the limited diagnosis information in the B-mode ultrasound (BUS) images. In this study, we develop a novel network ResNet-GAP that incorporates both localization and classification into a unified procedure. To enhance the performance of ResNet-GAP, we leverage stiffness information in the elastography ultrasound (EUS) modality by collaborative learning in the training stage. Specifically, a dual-channel ResNet-GAP is developed, one channel for BUS and the other for EUS. In each channel, multiple class activity maps (CAMs) are generated using a series of convolutional kernels of different sizes. The multi-scale consistency of the CAMs in both channels are further considered in network optimization. Experiments on 264 patients in this study show that the newly developed ResNet-GAP achieves an accuracy of 88.6%, a sensitivity of 95.3%, a specificity of 84.6%, and an AUC of 93.6% on the classification task, and a 1.0NLF of 87.9% on the localization task, which is better than some state-of-the-art approaches.
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7
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Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features. FORECASTING 2022. [DOI: 10.3390/forecast4010015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.
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Kim YS, Lee SE, Chang JM, Kim SY, Bae YK. Ultrasonographic morphological characteristics determined using a deep learning-based computer-aided diagnostic system of breast cancer. Medicine (Baltimore) 2022; 101:e28621. [PMID: 35060538 PMCID: PMC8772632 DOI: 10.1097/md.0000000000028621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/23/2021] [Indexed: 01/05/2023] Open
Abstract
To investigate the correlations between ultrasonographic morphological characteristics quantitatively assessed using a deep learning-based computer-aided diagnostic system (DL-CAD) and histopathologic features of breast cancer.This retrospective study included 282 women with invasive breast cancer (<5 cm; mean age, 54.4 [range, 29-85] years) who underwent surgery between February 2016 and April 2017. The morphological characteristics of breast cancer on B-mode ultrasonography were analyzed using DL-CAD, and quantitative scores (0-1) were obtained. Associations between quantitative scores and tumor histologic type, grade, size, subtype, and lymph node status were compared.Two-hundred and thirty-six (83.7%) tumors were invasive ductal carcinoma, 18 (6.4%) invasive lobular carcinoma, and 28 (9.9%) micropapillary, apocrine, and mucinous. The mean size was 1.8 ± 1.0 (standard deviation) cm, and 108 (38.3%) cases were node positive. Irregular shape score was associated with tumor size (P < .001), lymph nodes status (P = .001), and estrogen receptor status (P = .016). Not-circumscribed margin (P < .001) and hypoechogenicity (P = .003) scores correlated with tumor size, and non-parallel orientation score correlated with histologic grade (P = .024). Luminal A tumors exhibited more irregular features (P = .048) with no parallel orientation (P = .002), whereas triple-negative breast cancer showed a rounder/more oval and parallel orientation.Quantitative morphological characteristics of breast cancers determined using DL-CAD correlated with histopathologic features and could provide useful information about breast cancer phenotypes.
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Affiliation(s)
- Young Seon Kim
- Department of Radiology, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, South Korea
| | - Seung Eun Lee
- Department of Radiology, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, South Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Young Kyung Bae
- Department of Pathology, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, South Korea
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Marosán-Vilimszky P, Szalai K, Horváth A, Csabai D, Füzesi K, Csány G, Gyöngy M. Automated Skin Lesion Classification on Ultrasound Images. Diagnostics (Basel) 2021; 11:1207. [PMID: 34359290 PMCID: PMC8303815 DOI: 10.3390/diagnostics11071207] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (ROC) area under the curve (AUC) as well as the accuracy (ACC) were measured. The best classification was obtained for the classification of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over 90% and ACCs of over 85% obtained with all segmentation methods. Previous works have either not implemented FA ultrasound-based skin cancer classification (making diagnosis more lengthy and operator-dependent), or are unclear in their classification results. Furthermore, the current work is the first to assess the effect of implementing FA instead of SA classification, with FA classification never degrading performance (in terms of AUC or ACC) by more than 5%.
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Affiliation(s)
- Péter Marosán-Vilimszky
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083 Budapest, Hungary; (A.H.); (M.G.)
- Dermus Kft., Sopron út 64, 1116 Budapest, Hungary; (D.C.); (K.F.); (G.C.)
| | - Klára Szalai
- Department of Dermatology, Venereology and Dermatooncology, Semmelweis University, Mária u. 41, 1085 Budapest, Hungary;
| | - András Horváth
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083 Budapest, Hungary; (A.H.); (M.G.)
| | - Domonkos Csabai
- Dermus Kft., Sopron út 64, 1116 Budapest, Hungary; (D.C.); (K.F.); (G.C.)
| | - Krisztián Füzesi
- Dermus Kft., Sopron út 64, 1116 Budapest, Hungary; (D.C.); (K.F.); (G.C.)
| | - Gergely Csány
- Dermus Kft., Sopron út 64, 1116 Budapest, Hungary; (D.C.); (K.F.); (G.C.)
| | - Miklós Gyöngy
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, 1083 Budapest, Hungary; (A.H.); (M.G.)
- Dermus Kft., Sopron út 64, 1116 Budapest, Hungary; (D.C.); (K.F.); (G.C.)
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10
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Zhang P, Ma Z, Zhang Y, Chen X, Wang G. Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system. Gland Surg 2021; 10:2232-2245. [PMID: 34422594 PMCID: PMC8340346 DOI: 10.21037/gs-21-328] [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: 04/07/2021] [Accepted: 06/17/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND The automated breast ultrasound system (ABUS) is recognized as a valuable detection tool in addition to mammography. The purpose of this study was to propose a novel computer-aided diagnosis (CAD) system by extracting the textural features from ABUS images and to investigate the efficiency of using this CAD for breast cancer detection. METHODS This retrospective study involved 149 breast nodules [maximum diameter: mean size 18.89 mm, standard deviation (SD) 10.238, and range 5-59 mm] in 135. We assigned 3 novice readers (<3 years of experience and 3 experienced readers (≥10 years of experience to review the imaging data and stratify the 149 breast nodules as either malignant or benign. The Improved Inception V3 (II3) method was developed and used as an assistant tool to help the 6 readers to re-interpret the images. RESULTS Our method (II3) achieved an accuracy of 88.6% for the final result. The 3 novice readers had an average accuracy of 71.37%±4.067% while the 3 experienced readers was 83.03%±3.371% on the first-reading. With the help of II3 on the second-reading, the average accuracy of the novice readers increased to 84.13%±1.662% and the experienced readers increased to 89.50%±0.346%.The areas under the curve (AUCs) were similar compared with linear algorithms. The mean AUC of the novice readers was improved from 0.7751 (without II3) to 0.8232 (with II3). The mean AUC of the experienced readers was improved from 0.8939 (without II3) to 0.9211 (with II3). The mean AUC for all readers improved in both the second-reading mode (from 0.8345 to 0.8722, P=0.0081<0.05). CONCLUSIONS With the help of the II3, the diagnostic accuracy of the two groups were both improved, and II3 was more helpful for novice readers than for experienced readers. Our results showed that II3 is valuable in the differentiation of benign and malignant breast nodules and it also improves the experience and skill of some novice radiologists. The II3 cannot completely replace the influence of experience in the diagnostic process and will retain an auxiliary role in the clinic at present.
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Affiliation(s)
- Panpan Zhang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, China
| | - Zhaosheng Ma
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, China
| | - Yingtao Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiaodan Chen
- Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Gang Wang
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Zhejiang University, Linhai, China
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Classification of breast ultrasound with human-rating BI-RADS scores using mined diagnostic patterns and optimized neuro-network. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.104] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Pi Y, Chen Y, Deng D, Qi X, Li J, Lv Q, Yi Z. Automated diagnosis of multi-plane breast ultrasonography images using deep neural networks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.123] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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13
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Moon WK, Lee YW, Ke HH, Lee SH, Huang CS, Chang RF. Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 190:105361. [PMID: 32007839 DOI: 10.1016/j.cmpb.2020.105361] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 01/14/2020] [Accepted: 01/24/2020] [Indexed: 05/11/2023]
Abstract
Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD software has some problems (such as handcrafted features are hard to design; conventional CAD systems are difficult to confirm overfitting problems, etc.). In our study, we propose a CAD system for tumor diagnosis using an image fusion method combined with different image content representations and ensemble different CNN architectures on US images. The CNN-based method proposed in this study includes VGGNet, ResNet, and DenseNet. In our private dataset, there was a total of 1687 tumors that including 953 benign and 734 malignant tumors. The accuracy, sensitivity, specificity, precision, F1 score and the AUC of the proposed method were 91.10%, 85.14%, 95.77%, 94.03%, 89.36%, and 0.9697 respectively. In the open dataset (BUSI), there was a total of 697 tumors that including 437 benign lesions, 210 malignant tumors, and 133 normal images. The accuracy, sensitivity, specificity, precision, F1 score, and the AUC of the proposed method were 94.62%, 92.31%, 95.60%, 90%, 91.14%, and 0.9711. In conclusion, the results indicated different image content representations that affect the prediction performance of the CAD system, more image information improves the prediction performance, and the tumor shape feature can improve the diagnostic effect.
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Affiliation(s)
- Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea
| | - Yan-Wei Lee
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC
| | - Hao-Hsiang Ke
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, ROC
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, ROC; Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, Taiwan, ROC; MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan, ROC.
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14
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Two-stage ultrasound image segmentation using U-Net and test time augmentation. Int J Comput Assist Radiol Surg 2020; 15:981-988. [PMID: 32350786 DOI: 10.1007/s11548-020-02158-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 04/03/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE Detecting breast lesions using ultrasound imaging is an important application of computer-aided diagnosis systems. Several automatic methods have been proposed for breast lesion detection and segmentation; however, due to the ultrasound artefacts, and to the complexity of lesion shapes and locations, lesion or tumor segmentation from ultrasound breast images is still an open problem. In this paper, we propose using a lesion detection stage prior to the segmentation stage in order to improve the accuracy of the segmentation. METHODS We used a breast ultrasound imaging dataset which contained 163 images of the breast with either benign lesions or malignant tumors. First, we used a U-Net to detect the lesions and then used another U-Net to segment the detected region. We could show when the lesion is precisely detected, the segmentation performance substantially improves; however, if the detection stage is not precise enough, the segmentation stage also fails. Therefore, we developed a test-time augmentation technique to assess the detection stage performance. RESULTS By using the proposed two-stage approach, we could improve the average Dice score by 1.8% overall. The improvement was substantially more for images wherein the original Dice score was less than 70%, where average Dice score was improved by 14.5%. CONCLUSIONS The proposed two-stage technique shows promising results for segmentation of breast US images and has a much smaller chance of failure.
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15
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Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model. IEEE J Biomed Health Inform 2020; 24:984-993. [DOI: 10.1109/jbhi.2019.2960821] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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16
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Segmentation of breast ultrasound image with semantic classification of superpixels. Med Image Anal 2020; 61:101657. [PMID: 32032899 DOI: 10.1016/j.media.2020.101657] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 01/18/2020] [Accepted: 01/22/2020] [Indexed: 11/22/2022]
Abstract
Breast cancer is a great threat to females. Ultrasound imaging has been applied extensively in diagnosis of breast cancer. Due to the poor image quality, segmentation of breast ultrasound (BUS) image remains a very challenging task. Besides, BUS image segmentation is a crucial step for further analysis. In this paper, we proposed a novel method to segment the breast tumor via semantic classification and merging patches. The proposed method firstly selects two diagonal points to crop a region of interest (ROI) on the original image. Then, histogram equalization, bilateral filter and pyramid mean shift filter are adopted to enhance the image. The cropped image is divided into many superpixels using simple linear iterative clustering (SLIC). Furthermore, some features are extracted from the superpixels and a bag-of-words model can be created. The initial classification can be obtained by a back propagation neural network (BPNN). To refine preliminary result, k-nearest neighbor (KNN) is used for reclassification and the final result is achieved. To verify the proposed method, we collected a BUS dataset containing 320 cases. The segmentation results of our method have been compared with the corresponding results obtained by five existing approaches. The experimental results show that our method achieved competitive results compared to conventional methods in terms of TP and FP, and produced good approximations to the hand-labelled tumor contours with comprehensive consideration of all metrics (the F1-score = 89.87% ± 4.05%, and the average radial error = 9.95% ± 4.42%).
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Artificial Intelligence-Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI. Invest Radiol 2020; 54:325-332. [PMID: 30652985 DOI: 10.1097/rli.0000000000000544] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVES We investigated artificial intelligence (AI)-based classification of benign and malignant breast lesions imaged with a multiparametric breast magnetic resonance imaging (MRI) protocol with ultrafast dynamic contrast-enhanced MRI, T2-weighted, and diffusion-weighted imaging with apparent diffusion coefficient mapping. MATERIALS AND METHODS We analyzed 576 lesions imaged with MRI, including a consecutive set of biopsied malignant (368) and benign (149) lesions, and an additional set of 59 benign lesions proven by follow-up. We used deep learning methods to interpret ultrafast dynamic contrast-enhanced MRI and T2-weighted information. A random forests classifier combined the output with patient information (PI; age and BRCA status) and apparent diffusion coefficient values obtained from diffusion-weighted imaging to perform the final lesion classification. We used receiver operating characteristic (ROC) analysis to evaluate our results. Sensitivity and specificity were compared with the results of the prospective clinical evaluation by radiologists. RESULTS The area under the ROC curve was 0.811 when only ultrafast dynamics was used. The final AI system that combined all imaging information with PI resulted in an area under the ROC curve of 0.852, significantly higher than the ultrafast dynamics alone (P = 0.002). When operating at the same sensitivity level of radiologists in this dataset, this system produced 19 less false-positives than the number of biopsied benign lesions in our dataset. CONCLUSIONS Use of adjunct imaging and PI has a significant contribution in diagnostic performance of ultrafast breast MRI. The developed AI system for interpretation of multiparametric ultrafast breast MRI may improve specificity.
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Ayatollahi F, Shokouhi SB, Teuwen J. Differentiating benign and malignant mass and non-mass lesions in breast DCE-MRI using normalized frequency-based features. Int J Comput Assist Radiol Surg 2019; 15:297-307. [PMID: 31838643 DOI: 10.1007/s11548-019-02103-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 12/02/2019] [Indexed: 12/26/2022]
Abstract
PURPOSE In this study, we propose a new computer-aided diagnosis (CADx) to distinguish between malign and benign mass and non-mass lesions in breast DCE-MRI. For this purpose, we introduce new frequency textural features. METHODS In this paper, we propose novel normalized frequency-based features. These are obtained by applying the dual-tree complex wavelet transform to MRI slices containing a lesion for specific decomposition levels. The low-pass and band-pass frequency coefficients of the dual-tree complex wavelet transform represent the general shape and texture features, respectively, of the lesion. The extraction of these features is computationally efficient. We employ a support vector machine to classify the lesions, and investigate modified cost functions and under- and oversampling strategies to handle the class imbalance. RESULTS The proposed method has been tested on a dataset of 80 patients containing 103 lesions. An area under the curve of 0.98 for the mass and 0.94 for the non-mass lesions is obtained. Similarly, accuracies of 96.9% and 89.8%, sensitivities of 93.8% and 84.6% and specificities of 98% and 92.3% are obtained for the mass and non-mass lesions, respectively. CONCLUSION Normalized frequency-based features can characterize benign and malignant lesions efficiently in both mass- and non-mass-like lesions. Additionally, the combination of normalized frequency-based features and three-dimensional shape descriptors improves the CADx performance.
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Affiliation(s)
- Fazael Ayatollahi
- Electrical Engineering Department, Iran University of Science and Technology (IUST), Tehran, Iran.
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Shahriar B Shokouhi
- Electrical Engineering Department, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Jonas Teuwen
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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陈 杨, 李 加, 罗 燕, 颜 红, 蒋 华, 黄 林. [Thermoacoustic imaging and its application in breast cancer detection and therapy]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2019; 36:684-690. [PMID: 31441272 PMCID: PMC10319517 DOI: 10.7507/1001-5515.201901061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Indexed: 06/10/2023]
Abstract
Thermoacoustic imaging (TAI) is a new non-invasive, non-ionization and nondestructive modality capable of high microwave contrast and high ultrasound resolution, and it has attracted extensive attention in recent years. This review introduces the technical principle, imaging system and imaging characteristics of TAI, and then introduces the application of TAI for breast cancer detection as an example. This review introduces the advantages of TAI in solving corresponding clinical problems in view of its high resolution and high contrast. In addition, it also explains the roles of TAI in medical diagnosis and treatment. Finally, the potential applications of TAI in medical diagnosis is introduced from many aspects and multiple perspectives. The future development of TAI in the challenges of current medical diagnosis is also prospected.
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Affiliation(s)
- 杨 陈
- 电子科技大学 信息医学研究中心(成都 611731)Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China
- 四川大学华西医院 超声科(成都 610041)Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, P.R.China
| | - 加伍 李
- 电子科技大学 信息医学研究中心(成都 611731)Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China
| | - 燕 罗
- 电子科技大学 信息医学研究中心(成都 611731)Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China
| | - 红梅 颜
- 电子科技大学 信息医学研究中心(成都 611731)Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China
| | - 华北 蒋
- 电子科技大学 信息医学研究中心(成都 611731)Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China
- 四川大学华西医院 超声科(成都 610041)Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, P.R.China
| | - 林 黄
- 电子科技大学 信息医学研究中心(成都 611731)Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P.R.China
- 四川大学华西医院 超声科(成都 610041)Department of Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, P.R.China
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Zhang G, Yang Z, Gong L, Jiang S, Wang L, Cao X, Wei L, Zhang H, Liu Z. An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images. J Med Syst 2019; 43:181. [PMID: 31093830 DOI: 10.1007/s10916-019-1327-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 05/08/2019] [Indexed: 12/17/2022]
Abstract
As "the second eyes" of radiologists, computer-aided diagnosis systems play a significant role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a systematic survey of state-of-the-art techniques (both traditional techniques and deep learning techniques) for nodule diagnosis from computed tomography images. This review first introduces the current progress and the popular structure used for nodule diagnosis. In particular, we provide a detailed overview of the five major stages in the computer-aided diagnosis systems: data acquisition, nodule segmentation, feature extraction, feature selection and nodule classification. Second, we provide a detailed report of the selected works and make a comprehensive comparison between selected works. The selected papers are from the IEEE Xplore, Science Direct, PubMed, and Web of Science databases up to December 2018. Third, we discuss and summarize the better techniques used in nodule diagnosis and indicate the existing future challenges in this field, such as improving the area under the receiver operating characteristic curve and accuracy, developing new deep learning-based diagnosis techniques, building efficient feature sets (fusing traditional features and deep features), developing high-quality labeled databases with malignant and benign nodules and promoting the cooperation between medical organizations and academic institutions.
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Affiliation(s)
- Guobin Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Li Gong
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China. .,Centre for advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
| | - Lu Wang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Xi Cao
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Lin Wei
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Hongyun Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Ziqi Liu
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
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Breast tumor classification using different features of quantitative ultrasound parametric images. Int J Comput Assist Radiol Surg 2019; 14:623-633. [PMID: 30617720 DOI: 10.1007/s11548-018-01908-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 12/28/2018] [Indexed: 12/13/2022]
Abstract
RATIONALE AND OBJECTIVES The ultrasound B-mode-based morphological and texture analysis and Nakagami parametric imaging have been proposed to characterize breast tumors. Since these three feature categories of ultrasonic tissue characterization supply information on different physical characteristics of breast tumors, by combining the above methods is expected to provide more clues for classifying breast tumors. MATERIALS AND METHODS To verify the validity of the concept, raw data were obtained from 160 clinical cases. Six different types of morphological-feature parameters, four texture features, and the Nakagami parameter of benignancy and malignancy were extracted for evaluation. The Pearson's correlation matrix was used to calculate the correlation between different feature parameters. The fuzzy c-means clustering and stepwise regression techniques were utilized to determine the optimal feature set, respectively. The logistic regression, receiver operating characteristic curve, and support vector machine were used to estimate the diagnostic ability. RESULTS The best performance was obtained by combining morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter, with an accuracy of 89.4%, a specificity of 86.3%, a sensitivity of 92.5%, and an area under receiver operating characteristic curve of 0.96. There was no significant difference between using fuzzy c-means clustering, logistic regression, and support vector machine based on the optimal feature set for breast tumors classification. CONCLUSION Therefore, we verified that different physical ultrasonic features are functionally complementary and thus improve the performance in diagnosing breast tumors. Moreover, the optimal feature set had the maximum discriminating performance should be irrelative to the power of classifiers.
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Xu Y, Wang Y, Yuan J, Cheng Q, Wang X, Carson PL. Medical breast ultrasound image segmentation by machine learning. ULTRASONICS 2019; 91:1-9. [PMID: 30029074 DOI: 10.1016/j.ultras.2018.07.006] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 07/12/2018] [Accepted: 07/12/2018] [Indexed: 05/02/2023]
Abstract
Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women's health. Segmentation of breast ultrasound images into functional tissues can aid tumor localization, breast density measurement, and assessment of treatment response, which is important to the clinical diagnosis of breast cancer. However, manually segmenting the ultrasound images, which is skill and experience dependent, would lead to a subjective diagnosis; in addition, it is time-consuming for radiologists to review hundreds of clinical images. Therefore, automatic segmentation of breast ultrasound images into functional tissues has received attention in recent years, amidst the more numerous studies of detection and segmentation of masses. In this paper, we propose to use convolutional neural networks (CNNs) for segmenting breast ultrasound images into four major tissues: skin, fibroglandular tissue, mass, and fatty tissue, on three-dimensional (3D) breast ultrasound images. Quantitative metrics for evaluation of segmentation results including Accuracy, Precision, Recall, and F1measure, all reached over 80%, which indicates that the method proposed has the capacity to distinguish functional tissues in breast ultrasound images. Another metric called the Jaccard similarity index (JSI) yields an 85.1% value, outperforming our previous study using the watershed algorithm with 74.54% JSI value. Thus, our proposed method might have the potential to provide the segmentations necessary to assist the clinical diagnosis of breast cancer and improve imaging in other modes in medical ultrasound.
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Affiliation(s)
- Yuan Xu
- Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
| | - Yuxin Wang
- Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China
| | - Jie Yuan
- Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.
| | - Qian Cheng
- Department of Physics, Tongji University, Shanghai 200000, China
| | - Xueding Wang
- Department of Physics, Tongji University, Shanghai 200000, China; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Paul L Carson
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
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23
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Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey. BIOMED RESEARCH INTERNATIONAL 2018; 2018:5137904. [PMID: 29687000 PMCID: PMC5857346 DOI: 10.1155/2018/5137904] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 01/12/2018] [Accepted: 02/06/2018] [Indexed: 12/13/2022]
Abstract
The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.
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Huang Q, Huang X, Liu L, Lin Y, Long X, Li X. A case-oriented web-based training system for breast cancer diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:73-83. [PMID: 29428078 DOI: 10.1016/j.cmpb.2017.12.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 11/12/2017] [Accepted: 12/22/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor. METHODS We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists. RESULTS This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value < .05); meanwhile the senior radiologists show little improvement (p-value > .05). CONCLUSIONS The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner.
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Affiliation(s)
- Qinghua Huang
- School of Electronics and Information, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China; College of Information Engineering, Shenzhen University, Shenzhen 518060, China; School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
| | - Xianhai Huang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Longzhong Liu
- Department of Ultrasound, The Cancer Center of Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong, China.
| | - Yidi Lin
- Department of Ultrasound, The Cancer Center of Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong, China
| | - Xingzhang Long
- Department of Ultrasound, The Cancer Center of Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong, China
| | - Xuelong Li
- Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, China
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Lal M, Kaur L, Gupta S. Automatic segmentation of tumors in B-Mode breast ultrasound images using information gain based neutrosophic clustering. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2018; 26:209-225. [PMID: 29154313 DOI: 10.3233/xst-17313] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
BACKGROUND Since breast ultrasound images are of low contrast, contain inherent noise and shadowing effect due to its imaging process, segmentation of breast tumors depicting ultrasound image is a challenging task. Thus, a robust breast ultrasound image segmentation technique is inevitable. OBJECTIVE To develop an automatic lesion segmentation technique for breast ultrasound images. METHODS First, the technique automatically detects the suspicious tumor region of interest and discards the unwanted complex background regions. Next, based on the concept of information gain, the technique applies an existing neutrosophic clustering method to the detected region to segment the desired tumor area. The proposed technique computes information gain values from the local neighbourhood of each pixel, which is further used to update the membership values and the cluster centers for the neutrosophic clustering process. Integrating the concept of entropy and neutrosophic logic features into the technique enabled to generate better segmentation results. RESULTS Results of proposed method were compared both qualitatively and quantitatively with fuzzy c-means, neutrosophic c-means and neutrosophic ℓ-means clustering methods. It was observed that the proposed method outperformed the other three methods and yielded the best Mean (TP: 94.72, FP: 5.85, SI: 93.75, HD: 8.2, AMED: 2.4) and Standard deviation (TP: 3.2, FP: 3.7, SI: 3.8, HD: 2.6, AMED: 1.3) values for different quality metrics on the current set of breast ultrasound images. CONCLUSION Study demonstrated that the proposed technique is robust to the shadowing effect and produces more accurate segmentation of the tumor region, which is very similar to that visually segmented by Radiologist.
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Affiliation(s)
- Madan Lal
- Department of Computer Engineering, Punjabi University, Patiala, India
| | - Lakhwinder Kaur
- Department of Computer Engineering, Punjabi University, Patiala, India
| | - Savita Gupta
- Department of Computer Science and Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India
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Guo R, Lu G, Qin B, Fei B. Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. ULTRASOUND IN MEDICINE & BIOLOGY 2018; 44:37-70. [PMID: 29107353 PMCID: PMC6169997 DOI: 10.1016/j.ultrasmedbio.2017.09.012] [Citation(s) in RCA: 203] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/12/2017] [Accepted: 09/13/2017] [Indexed: 05/25/2023]
Abstract
Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. We summarize the study results seen in the literature and discuss their future directions. We also provide a review of ultrasound-guided, breast biopsy and the fusion of ultrasound with other imaging modalities, especially magnetic resonance imaging (MRI). For comparison, we also discuss the diagnostic performance of mammography, MRI, positron emission tomography and computed tomography for breast cancer diagnosis at the end of this review. New ultrasound imaging techniques, ultrasound-guided biopsy and the fusion of ultrasound with other modalities provide important tools for the management of breast patients.
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Affiliation(s)
- Rongrong Guo
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA; Department of Ultrasound, Shanxi Provincial Cancer Hospital, Taiyuan, Shanxi, China
| | - Guolan Lu
- The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA; The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, USA; Department of Mathematics and Computer Science, Emory College of Emory University, Atlanta, Georgia, USA; Winship Cancer Institute of Emory University, Atlanta, Georgia, USA.
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Meiburger KM, Acharya UR, Molinari F. Automated localization and segmentation techniques for B-mode ultrasound images: A review. Comput Biol Med 2017; 92:210-235. [PMID: 29247890 DOI: 10.1016/j.compbiomed.2017.11.018] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 11/30/2017] [Accepted: 11/30/2017] [Indexed: 12/14/2022]
Abstract
B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.
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Affiliation(s)
- Kristen M Meiburger
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy.
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Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography 2017; 37:217-225. [PMID: 28992680 PMCID: PMC6044219 DOI: 10.14366/usg.17046] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 08/14/2017] [Indexed: 02/04/2023] Open
Abstract
Purpose The purpose of this study was to evaluate the usefulness of applying computer-aided diagnosis (CAD) to breast ultrasound (US), depending on the reader's experience with breast imaging. Methods Between October 2015 and January 2016, two experienced readers obtained and analyzed the grayscale US images of 200 cases according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. They additionally applied CAD (S-Detect) to analyze the lesions and made a diagnostic decision subjectively, based on grayscale US with CAD. For the same cases, two inexperienced readers analyzed the grayscale US images using the BI-RADS lexicon and categories, added CAD, and came to a subjective diagnostic conclusion. We then compared the diagnostic performance depending on the reader's experience with breast imaging. Results The sensitivity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 91.7%, 75.0%, 75.0%, and 66.7%, respectively. The specificity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 76.6%, 71.8%, 78.2%, and 76.1%, respectively. When diagnoses were made subjectively in combination with CAD, the specificity significantly improved (76.6% to 80.3%) without a change in the sensitivity (91.7%) in the experienced readers. After subjective combination with CAD, both of the sensitivity and specificity improved in the inexperienced readers (75.0% to 83.3% and 71.8% to 77.1%). In addition, the area under the curve improved for both the experienced and inexperienced readers (0.84 to 0.86 and 0.73 to 0.80) after the addition of CAD. Conclusion CAD is more useful for less experienced readers. Combining CAD with breast US led to improved specificity for both experienced and inexperienced readers.
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Affiliation(s)
- Ji-Hye Choi
- Department of Radiology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Korea
| | - Bong Joo Kang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Ji Eun Baek
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun Sil Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images. BIOMED RESEARCH INTERNATIONAL 2017; 2017:9157341. [PMID: 28536703 PMCID: PMC5426079 DOI: 10.1155/2017/9157341] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 01/21/2017] [Accepted: 03/14/2017] [Indexed: 11/17/2022]
Abstract
Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%).
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Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MIB, Ramli ARB. Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI JOURNAL 2017; 16:113-137. [PMID: 28435432 PMCID: PMC5379115 DOI: 10.17179/excli2016-701] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 01/05/2017] [Indexed: 12/15/2022]
Abstract
Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.
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Affiliation(s)
- Afsaneh Jalalian
- Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra, Malaysia
| | - Syamsiah Mashohor
- Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra, Malaysia
| | - Rozi Mahmud
- Department of Imaging, Faculty of Medicine and Health Science, Universiti Putra, Malaysia
| | - Babak Karasfi
- Department of Computer Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - M. Iqbal B. Saripan
- Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra, Malaysia
| | - Abdul Rahman B. Ramli
- Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra, Malaysia
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Xiong H, Sultan LR, Cary TW, Schultz SM, Bouzghar G, Sehgal CM. The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images. ULTRASOUND : JOURNAL OF THE BRITISH MEDICAL ULTRASOUND SOCIETY 2017; 25:98-106. [PMID: 28567104 DOI: 10.1177/1742271x17690425] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 12/08/2016] [Indexed: 11/15/2022]
Abstract
PURPOSE To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. MATERIALS AND METHODS Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area (Oa ) between the margins, and area under the ROC curves (Az ). RESULTS The lesion size from leak-plugging segmentation correlated closely with that from manual tracing (R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings. CONCLUSION The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.
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Affiliation(s)
- Hui Xiong
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Laith R Sultan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore W Cary
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Schultz
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ghizlane Bouzghar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Chandra M Sehgal
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Breast ultrasound image segmentation: a survey. Int J Comput Assist Radiol Surg 2017; 12:493-507. [DOI: 10.1007/s11548-016-1513-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 12/15/2016] [Indexed: 10/20/2022]
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Prabusankarlal KM, Thirumoorthy P, Manavalan R. Segmentation of Breast Lesions in Ultrasound Images through Multiresolution Analysis Using Undecimated Discrete Wavelet Transform. ULTRASONIC IMAGING 2016; 38:384-402. [PMID: 26586725 DOI: 10.1177/0161734615615838] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Earliest detection and diagnosis of breast cancer reduces mortality rate of patients by increasing the treatment options. A novel method for the segmentation of breast ultrasound images is proposed in this work. The proposed method utilizes undecimated discrete wavelet transform to perform multiresolution analysis of the input ultrasound image. As the resolution level increases, although the effect of noise reduces, the details of the image also dilute. The appropriate resolution level, which contains essential details of the tumor, is automatically selected through mean structural similarity. The feature vector for each pixel is constructed by sampling intra-resolution and inter-resolution data of the image. The dimensionality of feature vectors is reduced by using principal components analysis. The reduced set of feature vectors is segmented into two disjoint clusters using spatial regularized fuzzy c-means algorithm. The proposed algorithm is evaluated by using four validation metrics on a breast ultrasound database of 150 images including 90 benign and 60 malignant cases. The algorithm produced significantly better segmentation results (Dice coef = 0.8595, boundary displacement error = 9.796, dvi = 1.744, and global consistency error = 0.1835) than the other three state of the art methods.
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Affiliation(s)
- K M Prabusankarlal
- Research and Development Centre, Bharathiar University, Coimbatore, India Department of Electronics & Communication, K.S.R. College of Arts & Science, Tiruchengode, India
| | - P Thirumoorthy
- Department of Electronics & Communication, Government Arts College, Dharmapuri, India
| | - R Manavalan
- Department of Computer Applications, K.S.R. College of Arts & Science, Tiruchengode, India
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Jeong JW, Yu D, Lee S, Chang JM. Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images. Healthc Inform Res 2016; 22:293-298. [PMID: 27895961 PMCID: PMC5116541 DOI: 10.4258/hir.2016.22.4.293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Revised: 09/25/2016] [Accepted: 09/28/2016] [Indexed: 12/03/2022] Open
Abstract
Objectives We propose an automatic breast mass detection algorithm in three-dimensional (3D) ultrasound (US) images using the Hough transform technique. Methods One hundred twenty-five cropped images containing 68 benign and 60 malignant masses are acquired with clinical diagnosis by an experienced radiologist. The 3D US images are masked, subsampled, contrast-adjusted, and median-filtered as preprocessing steps before the Hough transform is used. Thereafter, we perform 3D Hough transform to detect spherical hyperplanes in 3D US breast image volumes, generate Hough spheres, and sort them in the order of votes. In order to reduce the number of the false positives in the breast mass detection algorithm, the Hough sphere with a mean or grey level value of the centroid higher than the mean of the 3D US image is excluded, and the remaining Hough sphere is converted into a circumscribing parallelepiped cube as breast mass lesion candidates. Finally, we examine whether or not the generated Hough cubes were overlapping each other geometrically, and the resulting Hough cubes are suggested as detected breast mass candidates. Results An automatic breast mass detection algorithm is applied with mass detection sensitivity of 96.1% at 0.84 false positives per case, quite comparable to the results in previous research, and we note that in the case of malignant breast mass detection, every malignant mass is detected with false positives per case at a rate of 0.62. Conclusions The breast mass detection efficiency of our algorithm is assessed by performing a ROC analysis.
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Affiliation(s)
- Ji-Wook Jeong
- Department of Bio-medical IT Convergence Research, SW/Contents Research Laboratory, Electronics & Telecommunications Research Institute, Daejeon, Korea
| | - Donghoon Yu
- Medical Image Processing Team, Coreline Soft Co. Ltd., Seoul, Korea
| | - Sooyeul Lee
- Department of Bio-medical IT Convergence Research, SW/Contents Research Laboratory, Electronics & Telecommunications Research Institute, Daejeon, Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
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Araújo T, Abayazid M, Rutten MJCM, Misra S. Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS). Int J Med Robot 2016; 13. [DOI: 10.1002/rcs.1767] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 07/11/2016] [Accepted: 07/12/2016] [Indexed: 01/06/2023]
Affiliation(s)
- Teresa Araújo
- Department of Biomechanical Engineering; University of Twente; P. O. Box 217 7500 AE Enschede Overijsel Netherlands
- Faculty of Engineering of University of Porto; Rua Dr. Roberto Frias 4200-465 Porto Portugal
| | - Momen Abayazid
- Department of Biomechanical Engineering; University of Twente; P. O. Box 217 7500 AE Enschede Overijsel Netherlands
- Department of Radiology; Brigham and Women's Hospital and Harvard Medical School; 75 Francis Street Boston MA 02119 USA
| | - Matthieu J. C. M. Rutten
- Department of Radiology; Jeroen Bosch Hospital; Nieuwstraat 34 5211 NL's-Hertogenbosch The Netherlands
| | - Sarthak Misra
- Department of Biomechanical Engineering; University of Twente; P. O. Box 217 7500 AE Enschede Overijsel Netherlands
- Department of Biomedical Engineering; University of Groningen and University Medical Centre Groningen; Antonius Deusinglaan 1 9713 AV Groningen The Netherlands
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Tan T, Gubern-Mérida A, Borelli C, Manniesing R, van Zelst J, Wang L, Zhang W, Platel B, Mann RM, Karssemeijer N. Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model. Med Phys 2016; 43:4074. [DOI: 10.1118/1.4953206] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Sci Rep 2016; 6:24454. [PMID: 27079888 PMCID: PMC4832199 DOI: 10.1038/srep24454] [Citation(s) in RCA: 298] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 03/30/2016] [Indexed: 01/02/2023] Open
Abstract
This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.
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Shan J, Alam SK, Garra B, Zhang Y, Ahmed T. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:980-8. [PMID: 26806441 DOI: 10.1016/j.ultrasmedbio.2015.11.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 11/09/2015] [Accepted: 11/13/2015] [Indexed: 05/18/2023]
Abstract
This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions.
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Affiliation(s)
- Juan Shan
- Department of Computer Science, Seidenberg School of Computer Science and Information Systems, Pace University, New York, New York, USA.
| | - S Kaisar Alam
- Improlabs Pte Ltd, Valley Point, Singapore; Computational Biomedicine Imaging and Modeling Center (CBIM), Rutgers University, Piscataway, New Jersey, USA; Department of Electrical & Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh
| | - Brian Garra
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA; Washington DC Veterans Affairs Medical Center, Washington, DC, USA
| | - Yingtao Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tahira Ahmed
- Washington DC Veterans Affairs Medical Center, Washington, DC, USA
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Jeh SK, Kim SH, Choi JJ, Jung SS, Choe BJ, Park S, Park MS. Comparison of automated breast ultrasonography to handheld ultrasonography in detecting and diagnosing breast lesions. Acta Radiol 2016; 57:162-9. [PMID: 25766727 DOI: 10.1177/0284185115574872] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Accepted: 02/02/2015] [Indexed: 11/16/2022]
Abstract
BACKGROUND Automated breast ultrasonography (ABUS) is increasingly used as a screening tool. Several studies have demonstrated a similar diagnostic performance for ABUS compared with handheld ultrasonography (HHUS), but the overall results have been controversial. PURPOSE To compare the clinical utility of ABUS and HHUS for detection and diagnosis of breast lesions. MATERIAL AND METHODS ABUS and HHUS images of suspicious breast lesions were obtained for 173 consecutive women scheduled to undergo ultrasonography (US)-guided or stereotactic biopsy. There were a total of 206 lesions, 46 of which were malignant and 160 benign. Three breast radiologists took part in this study: two reviewed the ABUS images, and the third reviewed all of the images, ABUS and HHUS, as well as the patients' medical records. The biopsied-lesion-detection rates were obtained. Using the Breast Imaging Reporting and Data System (BI-RADS), the images of the biopsied lesions were evaluated. Factors affecting ABUS detectability were analyzed. RESULTS The overall detection rates were 83.0% for ABUS and 94.2% for HHUS. Ten lesions were not detected on either HHUS or ABUS and these were microcalcifications (one malignancy and nine benign lesions). Of the 194 HHUS-detected lesions, 169 were detected by ABUS and 25 benign were not. ABUS less frequently detected lesions of smaller size as well as those of benign appearance and lower final-assessment category (P = 0.011 and P < 0.0001, respectively). CONCLUSION ABUS detected all of the malignant lesions that were detected on HHUS. ABUS missed several smaller benign lesions.
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Affiliation(s)
- Su Kyung Jeh
- Department of Radiology, Hallym University Medical Center, The Hallym University of Korea, Chuncheon, Republic of Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae Jeong Choi
- Department of Radiology, Hallym University Medical Center, The Hallym University of Korea, Chuncheon, Republic of Korea
| | - Sang Sul Jung
- Department of General Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Byung Joo Choe
- Department of General Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sarah Park
- Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Mi Sun Park
- Department of Biostatistics, Clinical Research Coordinating Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2016. [DOI: 10.1007/978-3-319-21212-8_13] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Sudarshan VK, Mookiah MRK, Acharya UR, Chandran V, Molinari F, Fujita H, Ng KH. Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review. Comput Biol Med 2015; 69:97-111. [PMID: 26761591 DOI: 10.1016/j.compbiomed.2015.12.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 11/12/2015] [Accepted: 12/11/2015] [Indexed: 02/01/2023]
Abstract
Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
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Affiliation(s)
- Vidya K Sudarshan
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | | | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Malaysia; Department of Biomedical Engineering, School of Science and Technology, SIM University, 599491, Singapore
| | - Vinod Chandran
- School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4000, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - Hamido Fujita
- Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate 020-0693, Japan
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603, Malaysia
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Prabusankarlal KM, Thirumoorthy P, Manavalan R. Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2015. [DOI: 10.1186/s13673-015-0029-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
AbstractThe objective of this study is to assess the combined performance of textural and morphological features for the detection and diagnosis of breast masses in ultrasound images. We have extracted a total of forty four features using textural and morphological techniques. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. The performance of individual as well as combined features are assessed using accuracy(Ac), sensitivity(Se), specificity(Sp), Matthews correlation coefficient(MCC) and area AZ under receiver operating characteristics curve. The individual features produced classification accuracy in the range of 61.66% and 90.83% and when features from each category are combined, the accuracy is improved in the range of 79.16% and 95.83%. Moreover, the combination of gray level co-occurrence matrix (GLCM) and ratio of perimeters (P
ratio
) presented highest performance among all feature combinations (Ac 95.85%, Se 96%, Sp 91.46%, MCC 0.9146 and AZ 0.9444).The results indicated that the discrimination performance of a computer aided breast cancer diagnosis system increases when textural and morphological features are combined.
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Liu H, Tan T, van Zelst J, Mann R, Karssemeijer N, Platel B. Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound. J Med Imaging (Bellingham) 2014; 1:024501. [PMID: 26158036 DOI: 10.1117/1.jmi.1.2.024501] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 06/22/2014] [Accepted: 06/26/2014] [Indexed: 11/14/2022] Open
Abstract
We investigated the benefits of incorporating texture features into an existing computer-aided diagnosis (CAD) system for classifying benign and malignant lesions in automated three-dimensional breast ultrasound images. The existing system takes into account 11 different features, describing different lesion properties; however, it does not include texture features. In this work, we expand the system by including texture features based on local binary patterns, gray level co-occurrence matrices, and Gabor filters computed from each lesion to be diagnosed. To deal with the resulting large number of features, we proposed a combination of feature-oriented classifiers combining each group of texture features into a single likelihood, resulting in three additional features used for the final classification. The classification was performed using support vector machine classifiers, and the evaluation was done with 10-fold cross validation on a dataset containing 424 lesions (239 benign and 185 malignant lesions). We compared the classification performance of the CAD system with and without texture features. The area under the receiver operating characteristic curve increased from 0.90 to 0.91 after adding texture features ([Formula: see text]).
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Affiliation(s)
- Haixia Liu
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands ; University of Nottingham Malaysia Campus , School Of Computer Science, Room BB79, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Tao Tan
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Jan van Zelst
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Ritse Mann
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Nico Karssemeijer
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
| | - Bram Platel
- Radboud University Medical Centre , Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands
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Štepán-Buksakowska IL, Accurso JM, Diehn FE, Huston J, Kaufmann TJ, Luetmer PH, Wood CP, Yang X, Blezek DJ, Carter R, Hagen C, Hořínek D, Hejčl A, Roček M, Erickson BJ. Computer-aided diagnosis improves detection of small intracranial aneurysms on MRA in a clinical setting. AJNR Am J Neuroradiol 2014; 35:1897-902. [PMID: 24924543 DOI: 10.3174/ajnr.a3996] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND AND PURPOSE MRA is widely accepted as a noninvasive diagnostic tool for the detection of intracranial aneurysms, but detection is still a challenging task with rather low detection rates. Our aim was to examine the performance of a computer-aided diagnosis algorithm for detecting intracranial aneurysms on MRA in a clinical setting. MATERIALS AND METHODS Aneurysm detectability was evaluated retrospectively in 48 subjects with and without computer-aided diagnosis by 6 readers using a clinical 3D viewing system. Aneurysms ranged from 1.1 to 6.0 mm (mean = 3.12 mm, median = 2.50 mm). We conducted a multireader, multicase, double-crossover design, free-response, observer-performance study on sets of images from different MRA scanners by using DSA as the reference standard. Jackknife alternative free-response operating characteristic curve analysis with the figure of merit was used. RESULTS For all readers combined, the mean figure of merit improved from 0.655 to 0.759, indicating a change in the figure of merit attributable to computer-aided diagnosis of 0.10 (95% CI, 0.03-0.18), which was statistically significant (F(1,47) = 7.00, P = .011). Five of the 6 radiologists had improved performance with computer-aided diagnosis, primarily due to increased sensitivity. CONCLUSIONS In conditions similar to clinical practice, using computer-aided diagnosis significantly improved radiologists' detection of intracranial DSA-confirmed aneurysms of ≤6 mm.
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Affiliation(s)
- I L Štepán-Buksakowska
- From the Department of Radiology (I.L.Š.-B., F.E.D., J.H., T.J.K., P.H.L., C.P.W., B.J.E.) International Clinical Research Center (I.L.Š.-B., D.H., A.H.), St. Anne's University Hospital Brno, Brno, Czech Republic Department of Radiology (I.L.Š.-B., M.R.), Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - J M Accurso
- Department of Radiology (J.M.A.), Mayo Clinic, Jacksonville, Florida
| | - F E Diehn
- From the Department of Radiology (I.L.Š.-B., F.E.D., J.H., T.J.K., P.H.L., C.P.W., B.J.E.)
| | - J Huston
- From the Department of Radiology (I.L.Š.-B., F.E.D., J.H., T.J.K., P.H.L., C.P.W., B.J.E.)
| | - T J Kaufmann
- From the Department of Radiology (I.L.Š.-B., F.E.D., J.H., T.J.K., P.H.L., C.P.W., B.J.E.)
| | - P H Luetmer
- From the Department of Radiology (I.L.Š.-B., F.E.D., J.H., T.J.K., P.H.L., C.P.W., B.J.E.)
| | - C P Wood
- From the Department of Radiology (I.L.Š.-B., F.E.D., J.H., T.J.K., P.H.L., C.P.W., B.J.E.)
| | - X Yang
- Department of Information Services (X.Y., D.J.B.), Mayo Clinic, Rochester, Minnesota
| | - D J Blezek
- Department of Information Services (X.Y., D.J.B.), Mayo Clinic, Rochester, Minnesota
| | - R Carter
- Division of Biomedical Statistics and Informatics (R.C., C.H.)
| | - C Hagen
- Division of Biomedical Statistics and Informatics (R.C., C.H.)
| | - D Hořínek
- International Clinical Research Center (I.L.Š.-B., D.H., A.H.), St. Anne's University Hospital Brno, Brno, Czech Republic Department of Neurosurgery (D.H., A.H.), Masaryk Hospital, Ústí nad Labem, Czech Republic Department of Neurosurgery (D.H.), Central Military Hospital, Prague, Czech Republic
| | - A Hejčl
- International Clinical Research Center (I.L.Š.-B., D.H., A.H.), St. Anne's University Hospital Brno, Brno, Czech Republic Department of Neurosurgery (D.H., A.H.), Masaryk Hospital, Ústí nad Labem, Czech Republic
| | - M Roček
- Department of Radiology (I.L.Š.-B., M.R.), Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - B J Erickson
- From the Department of Radiology (I.L.Š.-B., F.E.D., J.H., T.J.K., P.H.L., C.P.W., B.J.E.)
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Cai H, Liu L, Peng Y, Wu Y, Li L. Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols. BMC Cancer 2014; 14:366. [PMID: 24885156 PMCID: PMC4036635 DOI: 10.1186/1471-2407-14-366] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Accepted: 05/12/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla. METHODS This retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status. RESULTS Three classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group. CONCLUSIONS The combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.
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Affiliation(s)
| | | | | | - Yaopan Wu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Imaging Diagnosis and Interventional Center, Guangzhou 510060, People's Republic of China.
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Clauser P, Londero V, Como G, Girometti R, Bazzocchi M, Zuiani C. Comparison between different imaging techniques in the evaluation of malignant breast lesions: can 3D ultrasound be useful? Radiol Med 2013; 119:240-8. [PMID: 24297584 DOI: 10.1007/s11547-013-0338-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2012] [Accepted: 01/10/2013] [Indexed: 10/26/2022]
Abstract
PURPOSE This study was done to assess the feasibility of three-dimensional ultrasonography (3D-US) for volume calculation of solid breast lesions. MATERIALS AND METHODS The volumes of 36 malignant lesions were measured using conventional 2D-US, 3D-US and magnetic resonance imaging (MRI) and compared with that obtained with histology (standard of reference). With 2D Ultrasouns, volume was estimated by measuring three diameters and calculating volume with the mathematical formula for spheres. With 3D-US, stored images were retrieved and boundaries of masses were manually outlined; volume calculation was performed with VOCAL software. For MRI, volume measurements were obtained with special software for 3D reconstructions, after each lesion had been manually outlined. Histology measured the three main diameters and the volume was estimated using the mathematical formula for spheres. Interclass correlation coefficient (ICC) and Bland-Altman plots were used to assess agreement between the volumes measured. RESULTS ICC indicated that a good level of concordance was identified between 3D-US and histology (0.79). According to the Bland-Altman analysis, limits of agreement of mean differences of the volumes measured with the three imaging modalities were comparable with histology: -2 ÷ 1.5 cm(3) for 3D-US; -2.3 ÷ 1.3 cm(3) for 2D-US and -2.2 ÷ 1.6 cm(3) for MRI. CONCLUSIONS 3D-US is a reliable method for the volumetric assessment of breast lesions. 3D-US is able to provide valuable information for the preoperative evaluation of lesions.
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Affiliation(s)
- Paola Clauser
- Institute of Diagnostic Radiology, University of Udine, P.le Santa Maria della Misericordia 15, 33100, Udine, Italy,
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Moon WK, Lo CM, Chang JM, Huang CS, Chen JH, Chang RF. Quantitative ultrasound analysis for classification of BI-RADS category 3 breast masses. J Digit Imaging 2013; 26:1091-8. [PMID: 23494603 PMCID: PMC3824917 DOI: 10.1007/s10278-013-9593-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
The accuracy of an ultrasound (US) computer-aided diagnosis (CAD) system was evaluated for the classification of BI-RADS category 3, probably benign masses. The US database used in this study contained 69 breast masses (21 malignant and 48 benign masses) that at blinded retrospective interpretation were assigned to BI-RADS category 3 by at least one of five radiologists. For computer-aided analysis, multiple morphology (shape, orientation, margin, lesions boundary, and posterior acoustic features) and texture (echo patterns) features based on BI-RADS lexicon were implemented, and the binary logistic regression model was used for classification. The receiver operating characteristic curve analysis was used for statistical analysis. The area under the curve (Az) of morphology, texture, and combined features were 0.90, 0.75, and 0.95, respectively. The combined features achieved the best performance and were significantly better than using texture features only (0.95 vs. 0.75, p value = 0.0163). The cut-off point at the sensitivity of 86 % (18/21), 95 % (20/21), and 100 % (21/21) achieved the specificity of 90 % (43/48), 73 % (35/48), and 33 % (16/48), respectively. In conclusion, the proposed CAD system has the potential to be used in upgrading malignant masses misclassified as BI-RADS category 3 on US by the radiologists.
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Affiliation(s)
- Woo Kyung Moon
- />Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Chung-Ming Lo
- />Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Jung Min Chang
- />Department of Radiology, Seoul National University Hospital, Seoul, South Korea
| | - Chiun-Sheng Huang
- />Department of Surgery, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jeon-Hor Chen
- />Department of Radiology, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Ruey-Feng Chang
- />Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- />Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
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Evaluation of the effect of computer-aided classification of benign and malignant lesions on reader performance in automated three-dimensional breast ultrasound. Acad Radiol 2013; 20:1381-8. [PMID: 24119350 DOI: 10.1016/j.acra.2013.07.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Revised: 07/25/2013] [Accepted: 07/29/2013] [Indexed: 11/23/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the effect of a newly developed computer-aided diagnosis (CAD) system on reader interpretation of breast lesions in automated three-dimensional (3D) breast ultrasound. MATERIALS AND METHODS A CAD system was developed to differentiate malignant lesions from benign lesions including automated lesion segmentation in three dimensions; extraction of lesion features such as spiculation, margin contrast, and posterior acoustic behavior; and a classification stage. Eighty-eight patients with breast lesions were included for an observer study: 47 lesions were malignant and 41 were benign. Eleven readers (seven radiologists and four residents) read the cases with and without CAD. We compared the performance of readers with and without CAD using receiver operating characteristic (ROC) analysis. RESULTS The CAD system had an area under the ROC curve (AUC) of 0.92 for discriminating benign and malignant lesions, whereas the unaided reader AUC ranged from 0.77 to 0.92. Mean performance of inexperienced readers improved when CAD was used (AUC = 0.85 versus 0.90; P = .007), whereas mean performance of experienced readers did not change with CAD (AUC = 0.89). CONCLUSIONS By using the CAD system for classification of lesions in automated 3D breast ultrasound, which on its own performed as good as the best readers, the performance of inexperienced readers improved while that of experienced readers remained unaffected.
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Martin L, Ruddlesden R, Makepeace C, Robinson L, Mistry T, Starritt H. Paediatric x-ray radiation dose reduction and image quality analysis. JOURNAL OF RADIOLOGICAL PROTECTION : OFFICIAL JOURNAL OF THE SOCIETY FOR RADIOLOGICAL PROTECTION 2013; 33:621-633. [PMID: 23803575 DOI: 10.1088/0952-4746/33/3/621] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Collaboration of multiple staff groups has resulted in significant reduction in the risk of radiation-induced cancer from radiographic x-ray exposure during childhood. In this study at an acute NHS hospital trust, a preliminary audit identified initial exposure factors. These were compared with European and UK guidance, leading to the introduction of new factors that were in compliance with European guidance on x-ray tube potentials. Image quality was assessed using standard anatomical criteria scoring, and visual grading characteristics analysis assessed the impact on image quality of changes in exposure factors. This analysis determined the acceptability of gradual radiation dose reduction below the European and UK guidance levels. Chest and pelvis exposures were optimised, achieving dose reduction for each age group, with 7%-55% decrease in critical organ dose. Clinicians confirmed diagnostic image quality throughout the iterative process. Analysis of images acquired with preliminary and final exposure factors indicated an average visual grading analysis result of 0.5, demonstrating equivalent image quality. The optimisation process and final radiation doses are reported for Carestream computed radiography to aid other hospitals in minimising radiation risks to children.
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Affiliation(s)
- L Martin
- Medical Physics and Bioengineering, Royal United Hospital, Combe Park, Bath, BA1 3NG, UK.
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Jose J, Prahladan A, Nair MS. Speckle reduction and contrast enhancement of ultrasound images using anisotropic diffusion with Jensen Shannon divergence operator. Biomed Eng Lett 2013. [DOI: 10.1007/s13534-013-0091-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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