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Mathai TS, Shen TC, Elton DC, Lee S, Lu Z, Summers RM. Detection of abdominopelvic lymph nodes in multi-parametric MRI. Comput Med Imaging Graph 2024; 114:102363. [PMID: 38447381 PMCID: PMC10981570 DOI: 10.1016/j.compmedimag.2024.102363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 02/16/2024] [Accepted: 02/17/2024] [Indexed: 03/08/2024]
Abstract
Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.
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Affiliation(s)
- Tejas Sudharshan Mathai
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA.
| | - Thomas C Shen
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Daniel C Elton
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Sungwon Lee
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
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Pan J, He Z, Li Y, Zeng W, Guo Y, Jia L, Jiang H, Chen W, Lu Y. Atypical architectural distortion detection in digital breast tomosynthesis: a multi-view computer-aided detection model with ipsilateral learning. Phys Med Biol 2023; 68:235006. [PMID: 37918341 DOI: 10.1088/1361-6560/ad092b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/01/2023] [Indexed: 11/04/2023]
Abstract
Objective.Breast architectural distortion (AD), a common imaging symptom of breast cancer, is associated with a particularly high rate of missed clinical detection. In clinical practice, atypical ADs that lack an obvious radiating appearance constitute most cases, and detection models based on single-view images often exhibit poor performance in detecting such ADs. Existing multi-view deep learning methods have overlooked the correspondence between anatomical structures across different views.Approach.To develop a computer-aided detection (CADe) model for AD detection that effectively utilizes the craniocaudal (CC) and mediolateral oblique (MLO) views of digital breast tomosynthesis (DBT) images, we proposed an anatomic-structure-based multi-view information fusion approach by leveraging the related anatomical structure information between these ipsilateral views. To obtain a representation that can effectively capture the similarity between ADs in images from ipsilateral views, our approach utilizes a Siamese network architecture to extract and compare information from both views. Additionally, we employed a triplet module that utilizes the anatomical structural relationship between the ipsilateral views as supervision information.Main results.Our method achieved a mean true positive fraction (MTPF) of 0.05-2.0, false positives (FPs) per volume of 64.40%, and a number of FPs at 80% sensitivity (FPs@0.8) of 3.5754; this indicates a 6% improvement in MPTF and 16% reduction in FPs@0.8 compared to the state-of-the-art baseline model.Significance.From our experimental results, it can be observed that the anatomic-structure-based fusion of ipsilateral view information contributes significantly to the improvement of CADe model performance for atypical AD detection based on DBT. The proposed approach has the potential to lead to earlier diagnosis and better patient outcomes.
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Affiliation(s)
- Jiawei Pan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, People's Republic of China
| | - Zilong He
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Yue Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Weixiong Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Yaya Guo
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Lixuan Jia
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Hai Jiang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Weiguo Chen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
- Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, People's Republic of China
- State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
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Classification of Breast Cancer Images by Implementing Improved DCNN with Artificial Fish School Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6785707. [PMID: 35242181 PMCID: PMC8888076 DOI: 10.1155/2022/6785707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/21/2022] [Accepted: 01/28/2022] [Indexed: 11/17/2022]
Abstract
Breast cancer is an important factor affecting human health. This issue has various diagnosis process which were evolved such as mammography, fine needle aspirate, and surgical biopsy. These techniques use pathological breast cancer images for diagnosis. Breast cancer surgery allows the forensic doctor to histologist to access the microscopic level of breast tissues. The conventional method uses an optimized radial basis neural network using a cuckoo search algorithm. Existing radial basis neural network techniques utilized feature extraction and reduction parts separately. It is proposed that it overcomes the CNN approach for all the feature extraction and classification process to reduce time complexity. In this proposed method, a convolutional neural network is proposed based on an artificial fish school algorithm. The breast cancer image dataset is taken from cancer imaging archives. In the preprocessing step of classification, the breast cancer image is filtered with the support of a wiener filter for classification. The convolutional neural network has set the intense data of an image and is used to remove the features. After executing the extraction procedure, the reduction process is performed to speed up the train and test data processing. Here, the artificial fish school optimization algorithm is utilized to give the direct training data to the deep convolutional neural network. The extraction, reduction, and classification of features are utilized in the single deep convolutional neural network process. In this process, the optimization technique helps to decrease the error rate and increases the performance efficiency by finding the number of epochs and training images to the Deep CNN. In this system, the normal, benign, and malignant tissues are predicted. By comparing the existing RBF technique with the cuckoo search algorithm, the presented model attains the outcome in the way of sensitivity, accuracy, specificity, F1 score, and recall.
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Liu Y, Zhou C, Zhang F, Zhang Q, Wang S, Zhou J, Sheng F, Wang X, Liu W, Wang Y, Yu Y, Lu G. Compare and contrast: Detecting mammographic soft-tissue lesions with C 2-Net. Med Image Anal 2021; 71:101999. [PMID: 33780707 DOI: 10.1016/j.media.2021.101999] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 07/29/2020] [Accepted: 02/02/2021] [Indexed: 10/22/2022]
Abstract
Detecting breast soft-tissue lesions including masses, structural distortions and asymmetries is of great importance due to the high risk leading to breast cancer. Most existing deep learning based approaches detect lesions with only unilateral images. However, multi-view mammogram images provide highly related and complementary information which helps to make the clinical analysis more comprehensive and reliable. In this paper, we propose a multi-view network for breast soft-tissue lesion detection called C2-Net (Compare and Contrast, C2) that fuses information across different views. The proposed model contains the following three modules. The spatial context enhancing (SCE) module compares ipsilateral views and extracts complementary features to model lesion inherent 3D structure. The multi-scale kernel pooling (MKP) module contrasts contralateral views with added misalignment tolerance. Finally, the logic guided fusion (LGF) module fuses multi-view features by enhancing logic modeling capacity. Experimental results on both the public DDSM dataset and the in-house multi-center dataset demonstrate that the proposed method has achieved state-of-the-art performance.
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Affiliation(s)
- Yuhang Liu
- AI Lab, Deepwise Healthcare, Beijing 100080, China
| | - Changsheng Zhou
- Medical Imaging Center, Nanjing Jinling Hospital Clinical School, Medical College, Nanjing University, Nanjing 210002, China
| | | | - Qianyi Zhang
- AI Lab, Deepwise Healthcare, Beijing 100080, China
| | - Siwen Wang
- AI Lab, Deepwise Healthcare, Beijing 100080, China
| | - Juan Zhou
- Department of Radiology, the Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Fugeng Sheng
- Department of Radiology, the Fifth Medical Centre, Chinese PLA General Hospital, Beijing 100071, China
| | - Xiaoqi Wang
- Department of Radiology, Gansu Provincial Cancer Hospital, Lanzhou 730050, China
| | - Wanhua Liu
- Department of Radiology, Zhongda Hospital, Southeast University, Nanjing 210009, China
| | - Yizhou Wang
- Center on Frontiers of Computing Studies, Dept. of Computer Science & Technology, Advanced Institute of Information Technology, Peking University, China
| | - Yizhou Yu
- AI Lab, Deepwise Healthcare, Beijing 100080, China; Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong.
| | - Guangming Lu
- Medical Imaging Center, Nanjing Jinling Hospital Clinical School, Medical College, Nanjing University, Nanjing 210002, China.
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Hirra I, Ahmad M, Hussain A, Ashraf MU, Saeed IA, Qadri SF, Alghamdi AM, Alfakeeh AS. Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling. IEEE ACCESS 2021; 9:24273-24287. [DOI: 10.1109/access.2021.3056516] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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Gnanasekaran VS, Joypaul S, Sundaram PM. A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms. Curr Med Imaging 2020; 16:639-652. [DOI: 10.2174/1573405615666190903141554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 07/08/2019] [Accepted: 07/17/2019] [Indexed: 01/22/2023]
Abstract
Breast cancer is leading cancer among women for the past 60 years. There are no effective
mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier
stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities
available for capturing the abnormalities in breasts, mammography is the most commonly used
technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in
analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for
diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms
used in the detection of breast cancer developed in recent years. We begin the review with
a concise introduction about the fundamental concepts related to mammograms and CAD systems.
We then focus on the techniques used in the diagnosis of breast cancer with mammograms.
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Affiliation(s)
| | - Sutha Joypaul
- AAA College of Engineering and Technology, Sivakasi 626123, Virudhunagar District, Tamil Nadu, India
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Shachor Y, Greenspan H, Goldberger J. A mixture of views network with applications to multi-view medical imaging. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Sapate S, Talbar S, Mahajan A, Sable N, Desai S, Thakur M. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2019.04.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Sun W, Tseng TLB, Qian W, Saltzstein EC, Zheng B, Yu H, Zhou S. A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 155:29-38. [PMID: 29512502 DOI: 10.1016/j.cmpb.2017.11.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Revised: 11/08/2017] [Accepted: 11/21/2017] [Indexed: 06/08/2023]
Abstract
PURPOSE To help improve efficacy of screening mammography and eventually establish an optimal personalized screening paradigm, this study aimed to develop and test a new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view of the negative screening mammograms. METHODS The dataset includes digital mammograms acquired from 392 women with two sequential full-field digital mammography examinations. All the first ("prior") sets of mammograms were interpreted as negative during the original reading. In the sequential ("current") screening, 202 were proved positive and 190 remained negative/benign. For each pair of the "prior" ipsilateral mammograms, we adaptively fused the image features computed from two views. Using four different types of image features, we built four elastic net support vector machine (EnSVM) based classifiers. Then, the initial prediction scores form the 4 EnSVMs were combined to build a final artificial neural network (ANN) classifier that produces the final risk prediction score. The performance of the new scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). RESULTS A total number of 466 features were initially extracted from each pair of ipsilateral mammograms. Among them, 51 were selected to build the EnSVM based prediction scheme. The AUC = 0.737 ± 0.052 was yielded using the new scheme. Applying an optimal operating threshold, the prediction sensitivity was 60.4% (122 of 202) and the specificity was 79.0% (150 of 190). CONCLUSION The study results showed moderately high positive association between computed risk scores using the "prior" negative mammograms and the actual outcome of the image-detectable breast cancers in the next subsequent screening examinations. The study also demonstrated that quantitative analysis of the ipsilateral views of the mammograms enabled to provide useful information in predicting near-term breast cancer risk.
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Affiliation(s)
- Wenqing Sun
- College of Engineering, University of Texas at El Paso, El Paso, TX, United States
| | - Tzu-Liang Bill Tseng
- College of Engineering, University of Texas at El Paso, El Paso, TX, United States
| | - Wei Qian
- College of Engineering, University of Texas at El Paso, El Paso, TX, United States; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China.
| | - Edward C Saltzstein
- University Breast Care Center at the Texas Tech University Health Sciences, El Paso, TX, United States
| | - Bin Zheng
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China; College of Engineering, University of Oklahoma, Norman, Oklahoma, United States
| | - Hui Yu
- Department of Radiology, Affiliated Hospital of Guiyang Medical University, Guiyang, China
| | - Shi Zhou
- Department of Radiology, Affiliated Hospital of Guiyang Medical University, Guiyang, China
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Carneiro G, Nascimento J, Bradley AP. Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:2355-2365. [PMID: 28920897 DOI: 10.1109/tmi.2017.2751523] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodology lies in the use of deep learning models for the problem of jointly classifying unregistered mammogram views and respective segmentation maps of breast lesions (i.e., masses and micro-calcifications). This is a holistic methodology that can classify a whole mammographic exam, containing the CC and MLO views and the segmentation maps, as opposed to the classification of individual lesions, which is the dominant approach in the field. We also demonstrate that the proposed system is capable of using the segmentation maps generated by automated mass and micro-calcification detection systems, and still producing accurate results. The semi-automated approach (using manually defined mass and micro-calcification segmentation maps) is tested on two publicly available data sets (INbreast and DDSM), and results show that the volume under ROC surface (VUS) for a 3-class problem (normal tissue, benign, and malignant) is over 0.9, the area under ROC curve (AUC) for the 2-class "benign versus malignant" problem is over 0.9, and for the 2-class breast screening problem (malignancy versus normal/benign) is also over 0.9. For the fully automated approach, the VUS results on INbreast is over 0.7, and the AUC for the 2-class "benign versus malignant" problem is over 0.78, and the AUC for the 2-class breast screening is 0.86.
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Mass classification of benign and malignant with a new twin support vector machine joint
$${l_{2,1}}$$
l
2
,
1
-norm. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0706-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Kim DH, Kim ST, Chang JM, Ro YM. Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis. Phys Med Biol 2017; 62:1009-1031. [PMID: 28081006 DOI: 10.1088/1361-6560/aa504e] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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13
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DeepCAD: A Computer-Aided Diagnosis System for Mammographic Masses Using Deep Invariant Features. COMPUTERS 2016. [DOI: 10.3390/computers5040028] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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14
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Bekker AJ, Shalhon M, Greenspan H, Goldberger J. Multi-View Probabilistic Classification of Breast Microcalcifications. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:645-53. [PMID: 26452277 DOI: 10.1109/tmi.2015.2488019] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we apply a multi-view-classifier for the task. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single benign or malignant decision. The proposed method was evaluated on a large number of cases from a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the advantage of the proposed multi-view classification algorithm that automatically learns the best way to combine the views.
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Kim DH, Kim ST, Ro YM. Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection. Phys Med Biol 2015; 60:8809-32. [PMID: 26529080 DOI: 10.1088/0031-9155/60/22/8809] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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16
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Ferreira P, Fonseca NA, Dutra I, Woods R, Burnside E. Predicting malignancy from mammography findings and image-guided core biopsies. INT J DATA MIN BIOIN 2015; 11:257-76. [PMID: 26333262 DOI: 10.1504/ijdmb.2015.067319] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The main goal of this work is to produce machine learning models that predict the outcome of a mammography from a reduced set of annotated mammography findings. In the study we used a dataset consisting of 348 consecutive breast masses that underwent image guided core biopsy performed between October 2005 and December 2007 on 328 female subjects. We applied various algorithms with parameter variation to learn from the data. The tasks were to predict mass density and to predict malignancy. The best classifier that predicts mass density is based on a support vector machine and has accuracy of 81.3%. The expert correctly annotated 70% of the mass densities. The best classifier that predicts malignancy is also based on a support vector machine and has accuracy of 85.6%, with a positive predictive value of 85%. One important contribution of this work is that our model can predict malignancy in the absence of the mass density attribute, since we can fill up this attribute using our mass density predictor.
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Kim ST, Kim DH, Ro YM. Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes. Phys Med Biol 2014; 59:5003-23. [PMID: 25119017 DOI: 10.1088/0031-9155/59/17/5003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In digital breast tomosynthesis, the three dimensional (3D) reconstructed volumes only provide quasi-3D structure information with limited resolution along the depth direction due to insufficient sampling in depth direction and the limited angular range. The limitation could seriously hamper the conventional 3D image analysis techniques for detecting masses because the limited number of projection views causes blurring in the out-of-focus planes. In this paper, we propose a novel mass detection approach using slice conspicuity in the 3D reconstructed digital breast volumes to overcome the above limitation. First, to overcome the limited resolution along the depth direction, we detect regions of interest (ROIs) on each reconstructed slice and separately utilize the depth directional information to combine the ROIs effectively. Furthermore, we measure the blurriness of each slice for resolving the degradation of performance caused by the blur in the out-of-focus plane. Finally, mass features are extracted from the selected in focus slices and analyzed by a support vector machine classifier to reduce the false positives. Comparative experiments have been conducted on a clinical data set. Experimental results demonstrate that the proposed approach outperforms the conventional 3D approach by achieving a high sensitivity with a small number of false positives.
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Affiliation(s)
- Seong Tae Kim
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291, Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea
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D S, E S. A CAD system to analyse mammogram images using fully complex-valued relaxation neural network ensembled classifier. J Med Eng Technol 2014; 38:359-66. [DOI: 10.3109/03091902.2014.942041] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Choi JY, Kim DH, Plataniotis KN, Ro YM. Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification. Phys Med Biol 2014; 59:3697-719. [DOI: 10.1088/0031-9155/59/14/3697] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Tanner C, van Schie G, Lesniak JM, Karssemeijer N, Székely G. Improved location features for linkage of regions across ipsilateral mammograms. Med Image Anal 2013; 17:1265-72. [DOI: 10.1016/j.media.2013.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2012] [Revised: 04/26/2013] [Accepted: 05/02/2013] [Indexed: 11/17/2022]
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van Schie G, Wallis MG, Leifland K, Danielsson M, Karssemeijer N. Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms. Med Phys 2013; 40:041902. [PMID: 23556896 DOI: 10.1118/1.4791643] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) which can make use of an existing CAD system for detection of breast masses in full-field digital mammography (FFDM). This approach has the advantage that large digital screening databases that are becoming available can be used for training. DBT is currently not used for screening which makes it hard to obtain sufficient data for training. METHODS The proposed CAD system is applied to reconstructed DBT volumes and consists of two stages. In the first stage, an existing 2D CAD system is applied to slabs composed of multiple DBT slices, after processing the slabs to a representation similar to that of the FFDM training data. In the second stage, the authors group detections obtained in the slabs that detect the same object and determine the 3D location of the grouped findings using one of three different approaches, including one that uses a set of features extracted from the DBT slabs. Experiments were conducted to determine performance of the CAD system, the optimal slab thickness for this approach and the best method to establish the 3D location. Experiments were performed using a database of 192 patients (752 DBT volumes). In 49 patients, one or more malignancies were present which were described as a mass, architectural distortion, or asymmetry. Free response receiver operating characteristic analysis and bootstrapping were used for statistical evaluation. RESULTS Best performance was obtained when slab thickness was in the range of 1-2 cm. Using the feature based 3D localization procedure developed in the study, accurate 3D localization could be obtained in most cases. Case sensitivities of 80% and 90% were achieved at 0.35 and 0.99 false positives per volume, respectively. CONCLUSIONS This study indicates that there may be a large benefit in using 2D mammograms for the development of CAD for DBT and that there is no need to exclusively limit development to DBT data.
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Affiliation(s)
- Guido van Schie
- Department of Radiology, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, The Netherlands.
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Rangayyan RM, Banik S, Desautels JEL. Detection of architectural distortion in prior mammograms via analysis of oriented patterns. J Vis Exp 2013. [PMID: 24022326 DOI: 10.3791/50341] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
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Affiliation(s)
- Rangaraj M Rangayyan
- Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary
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23
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Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng 2013; 15:327-57. [PMID: 23683087 DOI: 10.1146/annurev-bioeng-071812-152416] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.
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Affiliation(s)
- Maryellen L Giger
- Department of Radiology, University of Chicago, Chicago, IL 60637, USA.
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Mc Leod P, Verma B. Variable Hidden Neuron Ensemble for Mass Classification in Digital Mammograms [Application Notes]. IEEE COMPUT INTELL M 2013. [DOI: 10.1109/mci.2012.2228598] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Banik S, Rangayyan RM, Desautels JL. Computer-aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer. ACTA ACUST UNITED AC 2013. [DOI: 10.2200/s00463ed1v01y201212bme047] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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26
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Choi JY, Ro YM. Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms. Phys Med Biol 2012; 57:7029-52. [DOI: 10.1088/0031-9155/57/21/7029] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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27
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Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses. Eur Radiol 2012; 23:93-100. [PMID: 22772149 DOI: 10.1007/s00330-012-2562-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2012] [Revised: 05/19/2012] [Accepted: 05/28/2012] [Indexed: 10/28/2022]
Abstract
OBJECTIVES We developed a computer-aided detection (CAD) system aimed at decision support for detection of malignant masses and architectural distortions in mammograms. The effect of this system on radiologists' performance depends strongly on its standalone performance. The purpose of this study was to compare the standalone performance of this CAD system to that of radiologists. METHODS In a retrospective study, nine certified screening radiologists and three residents read 200 digital screening mammograms without the use of CAD. Performances of the individual readers and of CAD were computed as the true-positive fraction (TPF) at a false-positive fraction of 0.05 and 0.2. Differences were analysed using an independent one-sample t-test. RESULTS At a false-positive fraction of 0.05, the performance of CAD (TPF = 0.487) was similar to that of the certified screening radiologists (TPF = 0.518, P = 0.17). At a false-positive fraction of 0.2, CAD performance (TPF = 0.620) was significantly lower than the radiologist performance (TPF = 0.736, P <0.001). Compared to the residents, CAD performance was similar for all false-positive fractions. CONCLUSIONS The sensitivity of CAD at a high specificity was comparable to that of human readers. These results show potential for CAD to be used as an independent reader in breast cancer screening.
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28
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Velikova M, Lucas PJ, Samulski M, Karssemeijer N. A probabilistic framework for image information fusion with an application to mammographic analysis. Med Image Anal 2012; 16:865-75. [DOI: 10.1016/j.media.2012.01.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2011] [Revised: 11/20/2011] [Accepted: 01/16/2012] [Indexed: 10/14/2022]
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Jing H, Yang Y, Nishikawa RM. Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med Phys 2012; 39:676-85. [PMID: 22320777 DOI: 10.1118/1.3675600] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The authors propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CADx). The conventional approach in CADx is to first train a pattern-classifier based on a set of existing training samples and then apply this classifier to subsequent new cases. The purpose of this work is to improve the classification accuracy of a CADx classifier by making use of a set of known cases retrieved from a reference library that are similar to the case under consideration. METHODS In the proposed approach, the authors will first apply image-retrieval to obtain a set of lesion images from a library of known cases that have similar image features to a case being diagnosed (i.e., query). These retrieved cases are then used to optimize a pattern-classifier toward boosting its classification accuracy on the query case. The basic idea is to put more emphasis on those cases that are similar to the query. The proposed approach is demonstrated first using a linear classifier and then extended to a nonlinear classifier induced by kernel principal component analysis. RESULTS The proposed retrieval-driven approach was tested on a library of mammogram images from 1006 cases (646 benign and 360 malignant) obtained from multiple institutions and was demonstrated to yield significant improvement in classification performance. Measured by the area under the receiver operating characteristic curve (AUC), the case-adaptive approach could boost the classification performance of a linear classifier from AUC = 0.7415 to AUC = 0.7807; similar improvement was also obtained for a nonlinear classifier, with AUC boosted from 0.7527 to 0.7838. CONCLUSIONS Use of additional cases from a reference library that have similar image features can improve the classification accuracy of a CADx classifier on a query case. It can even outperform retraining the classifier with all the cases from the entire reference library. This implies that cases with similar image features are more relevant in defining the local decision boundary of the CADx classifier around the query.
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Affiliation(s)
- Hao Jing
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
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30
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Ferreira P, Fonseca NA, Dutra I, Woods R, Burnside E. Predicting Malignancy from Mammography Findings and Surgical Biopsies. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2011; 2011:10.1109/BIBM.2011.71. [PMID: 24363962 PMCID: PMC3866819 DOI: 10.1109/bibm.2011.71] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Breast screening is the regular examination of a woman's breasts to find breast cancer earlier. The sole exam approved for this purpose is mammography. Usually, findings are annotated through the Breast Imaging Reporting and Data System (BIRADS) created by the American College of Radiology. The BIRADS system determines a standard lexicon to be used by radiologists when studying each finding. Although the lexicon is standard, the annotation accuracy of the findings depends on the experience of the radiologist. Moreover, the accuracy of the classification of a mammography is also highly dependent on the expertise of the radiologist. A correct classification is paramount due to economical and humanitarian reasons. The main goal of this work is to produce machine learning models that predict the outcome of a mammography from a reduced set of annotated mammography findings. In the study we used a data set consisting of 348 consecutive breast masses that underwent image guided or surgical biopsy performed between October 2005 and December 2007 on 328 female subjects. The main conclusions are threefold: (1) automatic classification of a mammography, independent on information about mass density, can reach equal or better results than the classification performed by a physician; (2) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) a machine learning model can predict mass density with a quality as good as the specialist blind to biopsy, which is one of our main contributions. Our model can predict malignancy in the absence of the mass density attribute, since we can fill up this attribute using our mass density predictor.
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Affiliation(s)
| | | | - Inês Dutra
- CRACS-INESC Porto LA, Porto, Portugal ; CRACS-INESC Porto LA & DCC-FC, Universidade do Porto, Porto, Portugal
| | - Ryan Woods
- Department of Radiology, Johns Hopkins Hospital, Baltimore, MD, USA
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