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Moon WK, Chen IL, Yi A, Bae MS, Shin SU, Chang RF. Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:129-137. [PMID: 29903479 DOI: 10.1016/j.cmpb.2018.05.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 04/14/2018] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Axillary lymph node (ALN) status is a key indicator in assessing and determining the treatment strategy for patients with newly diagnosed breast cancer. Previous studies suggest that sonographic features of a primary tumor have the potential to predict ALN status in the preoperative staging of breast cancer. In this study, a computer-aided prediction (CAP) model as well as the tumor features for ALN metastasis in breast cancers were developed using breast ultrasound (US) images. METHODS A total of 249 malignant tumors were acquired from 247 female patients (ages 20-84 years; mean 55 ± 11 years) to test the differences between the non-metastatic (130) and metastatic (119) groups based on various features. After applying semi-automatic tumor segmentation, 69 quantitative features were extracted. The features included morphology and texture of tumors inside a ROI of breast US image. By the backward feature selection and linear logistic regression, the prediction model was constructed and established to estimate the likelihood of ALN metastasis for each sample collected. RESULTS In the experiments, the texture features showed higher performance for predicting ALN metastasis compared to morphology (Az, 0.730 vs 0.667). The difference, however, was not statistically significant (p-values > 0.05). Combining the textural and morphological features, the accuracy, sensitivity, specificity, and Az value achieved 75.1% (187/249), 79.0% (94/119), 71.5% (93/130), and 0.757, respectively. CONCLUSIONS The proposed CAP model, which combines textural and morphological features of primary tumor, may be a useful method to determine the ALN status in patients with breast cancer.
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Affiliation(s)
- Woo Kyung Moon
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - I-Ling Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Ann Yi
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Min Sun Bae
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Sung Ui Shin
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - 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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Moon WK, Huang YS, Lee YW, Chang SC, Lo CM, Yang MC, Bae MS, Lee SH, Chang JM, Huang CS, Lin YT, Chang RF. Computer-aided tumor diagnosis using shear wave breast elastography. ULTRASONICS 2017; 78:125-133. [PMID: 28342323 DOI: 10.1016/j.ultras.2017.03.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 01/16/2017] [Accepted: 03/13/2017] [Indexed: 06/06/2023]
Abstract
The shear wave elastography (SWE) uses the acoustic radiation force to measure the stiffness of tissues and is less operator dependent in data acquisition compared to strain elastography. However, the reproducibility of the result is still interpreter dependent. The purpose of this study is to develop a computer-aided diagnosis (CAD) method to differentiate benign from malignant breast tumors using SWE images. After applying the level set method to automatically segment the tumor contour and hue-saturation-value color transformation, SWE features including average tissue elasticity, sectional stiffness ratio, and normalized minimum distance for grouped stiffer pixels are calculated. Finally, the performance of CAD based on SWE features are compared with those based on B-mode ultrasound (morphologic and textural) features, and a combination of both feature sets to differentiate benign from malignant tumors. In this study, we use 109 biopsy-proved breast tumors composed of 57 benign and 52 malignant cases. The experimental results show that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic ROC curve (Az value) of CAD are 86.5%, 93.0%, 89.9%, and 0.905 for SWE features whereas they are 86.5%, 80.7%, 83.5% and 0.893 for B-mode features and 90.4%, 94.7%, 92.3% and 0.961 for the combined features. The Az value of combined feature set is significantly higher compared to the B-mode and SWE feature sets (p=0.0296 and p=0.0204, respectively). Our results suggest that the CAD based on SWE features has the potential to improve the performance of classifying breast tumors with US.
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Affiliation(s)
- Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Republic of Korea
| | - Yao-Sian Huang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yan-Wei Lee
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Shao-Chien Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chung-Ming Lo
- Graduate Institute of Biomedical Informatics Taipei Medical University, Taipei, Taiwan
| | - Min-Chun Yang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Min Sun Bae
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Republic of Korea
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Republic of Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Republic of Korea
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Ting Lin
- Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, Taiwan
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
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Moon WK, Chen IL, Chang JM, Shin SU, Lo CM, Chang RF. The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound. ULTRASONICS 2017; 76:70-77. [PMID: 28086107 DOI: 10.1016/j.ultras.2016.12.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 12/06/2016] [Accepted: 12/26/2016] [Indexed: 06/06/2023]
Abstract
Screening ultrasound (US) is increasingly used as a supplement to mammography in women with dense breasts, and more than 80% of cancers detected by US alone are 1cm or smaller. An adaptive computer-aided diagnosis (CAD) system based on tumor size was proposed to classify breast tumors detected at screening US images using quantitative morphological and textural features. In the present study, a database containing 156 tumors (78 benign and 78 malignant) was separated into two subsets of different tumor sizes (<1cm and ⩾1cm) to explore the improvement in the performance of the CAD system. After adaptation, the accuracies, sensitivities, specificities and Az values of the CAD for the entire database increased from 73.1% (114/156), 73.1% (57/78), 73.1% (57/78), and 0.790 to 81.4% (127/156), 83.3% (65/78), 79.5% (62/78), and 0.852, respectively. In the data subset of tumors larger than 1cm, the performance improved from 66.2% (51/77), 68.3% (28/41), 63.9% (23/36), and 0.703 to 81.8% (63/77), 85.4% (35/41), 77.8% (28/36), and 0.855, respectively. The proposed CAD system can be helpful to classify breast tumors detected at screening US.
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Affiliation(s)
- Woo Kyung Moon
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - I-Ling Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Jung Min Chang
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Sung Ui Shin
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Chung-Ming Lo
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 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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Chang RF, Lee CC, Lo CM. Computer-Aided Diagnosis of Different Rotator Cuff Lesions Using Shoulder Musculoskeletal Ultrasound. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:2315-2322. [PMID: 27381057 DOI: 10.1016/j.ultrasmedbio.2016.05.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Revised: 04/18/2016] [Accepted: 05/18/2016] [Indexed: 06/06/2023]
Abstract
The lifetime prevalence of shoulder pain approaches 70%, which is mostly attributable to rotator cuff lesions such as inflammation, calcific tendinitis and tears. On clinical examination, shoulder ultrasound is recommended for the detection of lesions. However, there exists inter-operator variability in diagnostic accuracy because of differences in the experience and expertise of operators. In this study, a computer-aided diagnosis (CAD) system was developed to assist ultrasound operators in diagnosing rotator cuff lesions and to improve the practicality of ultrasound examination. The collected cases included 43 cases of inflammation, 30 cases of calcific tendinitis and 26 tears. For each case, the lesion area and texture features were extracted from the entire lesions and combined in a multinomial logistic regression classifier for lesion classification. The proposed CAD achieved an accuracy of 87.9%. The individual accuracy of this CAD system was 88.4% for inflammation, 83.3% for calcific tendinitis and 92.3% for tears. Cohen's k was 0.798. On the basis of its diagnostic performance, clinical use of this CAD technique has promise.
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Affiliation(s)
- Ruey-Feng Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chung-Chien Lee
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Orthopedic Surgery, New Taipei City Hospital, New Taipei City, Taiwan; Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.
| | - Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
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Lo CM, Chan SW, Yang YW, Chang YC, Huang CS, Jou YS, Chang RF. Feasibility Testing: Three-dimensional Tumor Mapping in Different Orientations of Automated Breast Ultrasound. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:1201-1210. [PMID: 26825468 DOI: 10.1016/j.ultrasmedbio.2015.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 11/20/2015] [Accepted: 12/02/2015] [Indexed: 06/05/2023]
Abstract
A tumor-mapping algorithm was proposed to identify the same regions in different passes of automated breast ultrasound (ABUS). A total of 53 abnormal passes with 41 biopsy-proven tumors and 13 normal passes were collected. After computer-aided tumor detection, a mapping pair was composed of a detected region in one pass and another region in another pass. Location criteria, including the radial position as on a clock, the relative distance and the distance to the nipple, were used to extract mapping pairs with close regions. Quantitative intensity, morphology, texture and location features were then combined in a classifier for further classification. The performance of the classifier achieved a mapping rate of 80.39% (41/51), with an error rate of 5.97% (4/67). The trade-offs between the mapping and error rates were evaluated, and Az = 0.9094 was obtained. The proposed tumor-mapping algorithm was capable of automatically providing location correspondence information that would be helpful in reviews of ABUS examinations.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Si-Wa Chan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ya-Wen Yang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Yi-Sheng Jou
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 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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Summers SM, Chin EJ, Long BJ, Grisell RD, Knight JG, Grathwohl KW, Ritter JL, Morgan JD, Salinas J, Blackbourne LH. Computerized Diagnostic Assistant for the Automatic Detection of Pneumothorax on Ultrasound: A Pilot Study. West J Emerg Med 2016; 17:209-15. [PMID: 26973754 PMCID: PMC4786248 DOI: 10.5811/westjem.2016.1.28087] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 01/08/2016] [Accepted: 01/15/2016] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Bedside thoracic ultrasound (US) can rapidly diagnose pneumothorax (PTX) with improved accuracy over the physical examination and without the need for chest radiography (CXR); however, US is highly operator dependent. A computerized diagnostic assistant was developed by the United States Army Institute of Surgical Research to detect PTX on standard thoracic US images. This computer algorithm is designed to automatically detect sonographic signs of PTX by systematically analyzing B-mode US video clips for pleural sliding and M-mode still images for the seashore sign. This was a pilot study to estimate the diagnostic accuracy of the PTX detection computer algorithm when compared to an expert panel of US trained physicians. METHODS This was a retrospective study using archived thoracic US obtained on adult patients presenting to the emergency department (ED) between 5/23/2011 and 8/6/2014. Emergency medicine residents, fellows, attending physicians, physician assistants, and medical students performed the US examinations and stored the images in the picture archive and communications system (PACS). The PACS was queried for all ED bedside US examinations with reported positive PTX during the study period along with a random sample of negatives. The computer algorithm then interpreted the images, and we compared the results to an independent, blinded expert panel of three physicians, each with experience reviewing over 10,000 US examinations. RESULTS Query of the PACS system revealed 146 bedside thoracic US examinations for analysis. Thirteen examinations were indeterminate and were excluded. There were 79 true negatives, 33 true positives, 9 false negatives, and 12 false positives. The test characteristics of the algorithm when compared to the expert panel were sensitivity 79% (95 % CI [63-89]) and specificity 87% (95% CI [77-93]). For the 20 images scored as highest quality by the expert panel, the algorithm demonstrated 100% sensitivity (95% CI [56-100]) and 92% specificity (95% CI [62-100]). CONCLUSION This novel computer algorithm has potential to aid clinicians with the identification of the sonographic signs of PTX in the absence of expert physician sonographers. Further refinement and training of the algorithm is still needed, along with prospective validation, before it can be utilized in clinical practice.
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Affiliation(s)
- Shane M Summers
- Brooke Army Medical Center, Department of Emergency Medicine, San Antonio, Texas
| | - Eric J Chin
- Brooke Army Medical Center, Department of Emergency Medicine, San Antonio, Texas
| | - Brit J Long
- Brooke Army Medical Center, Department of Emergency Medicine, San Antonio, Texas
| | - Ronald D Grisell
- United States Army Institute of Surgical Research, San Antonio, Texas
| | - John G Knight
- Brooke Army Medical Center, Department of Emergency Medicine, San Antonio, Texas
| | - Kurt W Grathwohl
- Brooke Army Medical Center, Department of Pulmonary/Critical Care, San Antonio, Texas
| | - John L Ritter
- Brooke Army Medical Center, Department of Radiology, San Antonio, Texas
| | - Jeffrey D Morgan
- Brooke Army Medical Center, Department of Emergency Medicine, San Antonio, Texas
| | - Jose Salinas
- United States Army Institute of Surgical Research, San Antonio, Texas
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Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography. Comput Biol Med 2015; 64:91-100. [DOI: 10.1016/j.compbiomed.2015.06.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 06/17/2015] [Accepted: 06/17/2015] [Indexed: 12/21/2022]
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Lo CM, Moon WK, Huang CS, Chen JH, Yang MC, Chang RF. Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses. ULTRASOUND IN MEDICINE & BIOLOGY 2015; 41:2039-2048. [PMID: 25843514 DOI: 10.1016/j.ultrasmedbio.2015.03.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Revised: 02/22/2015] [Accepted: 03/01/2015] [Indexed: 06/04/2023]
Abstract
Radiologists likely incorrectly classify benign masses as Breast Imaging Reporting and Data System (BI-RADS) category 3. A computer-aided diagnosis (CAD) system was developed in this study as a second viewer to avoid misclassification of carcinomas. Sixty-nine biopsy-proven BI-RADS category 3 masses, including 21 malignant and 48 benign masses, were used to evaluate the CAD system. To improve the texture features, gray-scale variations between images were reduced by transforming pixels into intensity-invariant ranklet coefficients. The textures of the tumor and speckle pixels were extracted from the transformed ranklet images to provide more robust features than in conventional CAD systems. As a result, tumor texture and speckle texture with ranklet transformation achieved significantly better areas under the receiver operating characteristic curve (Az) compared with those without ranklet transformation (Az = 0.83 vs. 0.58 and Az = 0.80 vs. 0.56, p value < 0.05). The improved CAD system can be a second reader to confirm the classification of BI-RADS category 3 masses.
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Affiliation(s)
- Chung-Ming Lo
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jeon-Hor Chen
- Center for Functional Onco-Imaging and Department of Radiologic Science, University of California at Irvine, Irvine, California, USA; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Min-Chun Yang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 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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Lo CM, Chen YP, Chang YC, Lo C, Huang CS, Chang RF. Computer-Aided Strain Evaluation for Acoustic Radiation Force Impulse Imaging of Breast Masses. ULTRASONIC IMAGING 2014; 36:151-166. [PMID: 24894867 DOI: 10.1177/0161734613520599] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Acoustic radiation force impulse (ARFI) is a newly developed elastography technique that uses acoustic radiation force to provide additional stiffness information to conventional sonography. A computer-aided diagnosis (CAD) system was proposed to automatically specify the tumor boundaries in ARFI images and quantify the statistical stiffness information to reduce user dependence. The level-set segmentation was used to delineate tumor boundaries in B-mode images, and the segmented boundaries were then mapped to the corresponding area in ARFI images for a gray-scale calculation. A total of 61 benign and 51 malignant tumors were evaluated in the experiment. The CAD system based on the proposed ARFI features achieved an accuracy of 80% (90/112), a sensitivity of 80% (41/51), and a specificity of 80% (49/61), which is significantly better than that of the quantitative B-mode features (p < 0.05). The ARFI features were further combined with the B-mode features, including shape and texture features, to further improve performance (area under the curve [AUC], 0.90 vs. 0.86). In conclusion, the CAD system based on the proposed ARFI features is a promising and efficient diagnostic method.
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Affiliation(s)
- Chung-Ming Lo
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China
| | - Yen-Po Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China
| | - Chiao Lo
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China
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Lo CM, Chen RT, Chang YC, Yang YW, Hung MJ, Huang CS, Chang RF. Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1503-1511. [PMID: 24718570 DOI: 10.1109/tmi.2014.2315206] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Automated whole breast ultrasound (ABUS) is becoming a popular screening modality for whole breast examination. Compared to conventional handheld ultrasound, ABUS achieves operator-independent and is feasible for mass screening. However, reviewing hundreds of slices in an ABUS image volume is time-consuming. A computer-aided detection (CADe) system based on watershed transform was proposed in this study to accelerate the reviewing. The watershed transform was applied to gather similar tissues around local minima to be homogeneous regions. The likelihoods of being tumors of the regions were estimated using the quantitative morphology, intensity, and texture features in the 2-D/3-D false positive reduction (FPR). The collected database comprised 68 benign and 65 malignant tumors. As a result, the proposed system achieved sensitivities of 100% (133/133), 90% (121/133), and 80% (107/133) with FPs/pass of 9.44, 5.42, and 3.33, respectively. The figure of merit of the combination of three feature sets is 0.46 which is significantly better than that of other feature sets ( [Formula: see text]). In summary, the proposed CADe system based on the multi-dimensional FPR using the integrated feature set is promising in detecting tumors in ABUS images.
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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: 40] [Impact Index Per Article: 3.6] [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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