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Wang J, Sun H, Jiang K, Cao W, Chen S, Zhu J, Yang X, Zheng J. CAPNet: Context attention pyramid network for computer-aided detection of microcalcification clusters in digital breast tomosynthesis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107831. [PMID: 37783114 DOI: 10.1016/j.cmpb.2023.107831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 12/25/2022] [Accepted: 09/23/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer-aided detection (CADe) of microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) is crucial in the early diagnosis of breast cancer. Although convolutional neural network (CNN)-based detection models have achieved excellent performance in medical lesion detection, they are subject to some limitations in MC detection: 1) Most existing models employ the feature pyramid network (FPN) for multi-scale object detection; however, the rough feature sharing between adjacent layers in the FPN may limit the detection ability for small and low-contrast MCs; and 2) the MCs region only accounts for a small part of the annotation box, so the features extracted indiscriminately within the whole box may easily be affected by the background. In this paper, we develop a novel CNN-based CADe method to alleviate the impacts of the above limitations for the accurate and rapid detection of MCs in DBT. METHODS The proposed method has two parts: a novel context attention pyramid network (CAPNet) for intra-layer MC detection in two-dimensional (2D) slices and a three-dimensional (3D) aggregation procedure for aggregating 2D intra-layer MCs into a 3D result according to their connectivity in 3D space. The proposed CAPNet is based on an anchor-free and one-stage detection architecture and contains a context feature selection fusion (CFSF) module and a microcalcification response (MCR) branch. The CFSF module can efficiently enrich shallow layers' features by the complementary selection of local context features, aiming to reduce the missed detection of small and low-contrast MCs. The MCR branch is a one-layer branch parallel to the classification branch, which can alleviate the influence of the background region within the annotation box on feature extraction and enhance the ability of the model to distinguish MCs from normal breast tissue. RESULTS We performed a comparison experiment on an in-house clinical dataset with 648 DBT volumes, and the proposed method achieved impressive performance with a sensitivity of 91.56 % at 1 false positive per DBT volume (FPs/volume) and 93.51 % at 2 FPs/volume, outperforming other representative detection models. CONCLUSIONS The experimental results indicate that the proposed method is effective in the detection of MCs in DBT. This method can provide objective, accurate, and quick diagnostic suggestions for radiologists, presenting potential clinical value for early breast cancer screening.
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Affiliation(s)
- Jingkun Wang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Haotian Sun
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Ke Jiang
- Gusu School, Nanjing Medical University, Suzhou 215006, China; Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, China
| | - Weiwei Cao
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Shuangqing Chen
- Gusu School, Nanjing Medical University, Suzhou 215006, China; Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, China
| | - Jianbing Zhu
- Suzhou Science & Technology Town Hospital, Gusu School, Nanjing Medical University, Suzhou 215153, China
| | - Xiaodong Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jian Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs. J Imaging 2022; 8:jimaging8090231. [PMID: 36135397 PMCID: PMC9503015 DOI: 10.3390/jimaging8090231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/26/2022] [Accepted: 08/04/2022] [Indexed: 11/30/2022] Open
Abstract
Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images.
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Yan Y, Liu Y, Wu Y, Zhang H, Zhang Y, Meng L. Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103299] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Chan HP, Helvie MA, Klein KA, McLaughlin C, Neal CH, Oudsema R, Rahman WT, Roubidoux MA, Hadjiiski LM, Zhou C, Samala RK. Effect of Dose Level on Radiologists' Detection of Microcalcifications in Digital Breast Tomosynthesis: An Observer Study with Breast Phantoms. Acad Radiol 2022; 29 Suppl 1:S42-S49. [PMID: 32950384 DOI: 10.1016/j.acra.2020.07.038] [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: 05/19/2020] [Revised: 07/30/2020] [Accepted: 07/30/2020] [Indexed: 11/16/2022]
Abstract
OBJECTIVES To compare radiologists' sensitivity, confidence level, and reading efficiency of detecting microcalcifications in digital breast tomosynthesis (DBT) at two clinically relevant dose levels. MATERIALS AND METHODS Six 5-cm-thick heterogeneous breast phantoms embedded with a total of 144 simulated microcalcification clusters of four speck sizes were imaged at two dose modes by a clinical DBT system. The DBT volumes at the two dose levels were read independently by six MQSA radiologists and one fellow with 1-33 years (median 12 years) of experience in a fully-crossed counter-balanced manner. The radiologist located each potential cluster and rated its conspicuity and his/her confidence that the marked location contained a cluster. The differences in the results between the two dose modes were analyzed by two-tailed paired t-test. RESULTS Compared to the lower-dose mode, the average glandular dose in the higher-dose mode for the 5-cm phantoms increased from 1.34 to 2.07 mGy. The detection sensitivity increased for all speck sizes and significantly for the two smaller sizes (p <0.05). An average of 13.8% fewer false positive clusters was marked. The average conspicuity rating and the radiologists' confidence level were higher for all speck sizes and reached significance (p <0.05) for the three larger sizes. The average reading time per detected cluster reduced significantly (p <0.05) by an average of 13.2%. CONCLUSION For a 5-cm-thick breast, an increase in average glandular dose from 1.34 to 2.07 mGy for DBT imaging increased the conspicuity of microcalcifications, improved the detection sensitivity by radiologists, increased their confidence levels, reduced false positive detections, and increased the reading efficiency.
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Affiliation(s)
- Heang-Ping Chan
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842.
| | - Mark A Helvie
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Katherine A Klein
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Carol McLaughlin
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Colleen H Neal
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Rebecca Oudsema
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - W Tania Rahman
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Marilyn A Roubidoux
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Lubomir M Hadjiiski
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Chuan Zhou
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Ravi K Samala
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
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Improved U-net MALF model for lesion segmentation in breast ultrasound images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102721] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11062503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%.
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Ren L, Liu Y, Tong Y, Cao X, Wu Y. Calcification segmentation based on a different scales superpixels saliency detection algorithm. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:3404-3412. [PMID: 32977997 DOI: 10.1016/j.ultrasmedbio.2020.08.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 07/05/2020] [Accepted: 08/09/2020] [Indexed: 06/11/2023]
Abstract
Accurate detection of breast tumor calcifications is of great significance in assisting doctors' diagnosis to improve the accuracy of breast cancer early detection. In this article, a different scale of superpixels saliency detection algorithm is used to segment calcifications in breast tumor ultrasound images based on a simple linear iterative cluster. First, a multi-scale saliency segmentation algorithm was used to divide the tumor region of different sizes and weak calcification (Wca) was extracted according to uneven gray distribution and texture contrast between regions. Second, based on single-scale superpixel segmentation of the original image, the strong calcification extraction map was calculated by measuring gray value difference and calcification gray distance features. Finally, the final calcification extraction map was obtained by combining the strong and weak calcification extraction maps. The detection algorithm proposed in this article could effectively detect calcifications in breast ultrasound images.
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Affiliation(s)
- Li Ren
- Electronic and Communication Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China 210003.
| | - Yangyang Liu
- College of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China 211167
| | - Ying Tong
- College of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China 211167
| | - Xuehong Cao
- Electronic and Communication Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China 210003; College of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China 211167
| | - Yiyun Wu
- Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China 210029
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Sutphin C, Olson E, Motai Y, Lee SJ, Kim JG, Takabe K. Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:4300312. [PMID: 31497411 PMCID: PMC6726464 DOI: 10.1109/jtehm.2019.2935721] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 06/12/2019] [Accepted: 08/07/2019] [Indexed: 11/18/2022]
Abstract
Noncancerous breast tissue and cancerous breast tissue have different elastic properties. In particular, cancerous breast tumors are stiff when compared to the noncancerous surrounding tissue. This difference in elasticity can be used as a means for detection through the method of elastographic tomosynthesis by means of physical modulation. This paper deals with a method to visualize elasticity of soft tissues, particularly breast tissues, via x-ray tomosynthesis. X-ray tomosynthesis is now used to visualize breast tissues with better resolution than the conventional single-shot mammography. The advantage of X-ray tomosynthesis over X-ray CT is that fewer projections are needed than CT to perform the reconstruction, thus radiation exposure and cost are both reduced. Two phantoms were used for the testing of this method, a physical phantom and an in silico phantom. The standard root mean square error in the tomosynthesis for the physical phantom was 2.093 and the error in the in silico phantom was negligible. The elastographs were created through the use of displacement and strain graphing. A Gaussian Mixture Model with an expectation–maximization clustering algorithm was applied in three dimensions with an error of 16.667%. The results of this paper have been substantial when using phantom data. There are no equivalent comparisons yet in 3D x-ray elastographic tomosynthesis. Tomosynthesis with and without physical modulation in the 3D elastograph can identify feature groupings used for biopsy. The studies have potential to be applied to human test data used as a guide for biopsy to improve accuracy of diagnosis results. Further research on this topic could prove to yield new techniques for human patient diagnosis purposes.
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Affiliation(s)
- Corey Sutphin
- 1Department of Electrical and Computer EngineeringVirginia Commonwealth UniversityRichmondVA23284USA
| | - Eric Olson
- 1Department of Electrical and Computer EngineeringVirginia Commonwealth UniversityRichmondVA23284USA
| | - Yuichi Motai
- 1Department of Electrical and Computer EngineeringVirginia Commonwealth UniversityRichmondVA23284USA
| | - Suk Jin Lee
- 2TSYS School of Computer ScienceColumbus State UniversityColumbusGA31907USA
| | - Jae G Kim
- 3Imaging Software LabNano-ray Co., Ltd.Daegu601-604South Korea
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Samala RK, Hadjiiski L, Helvie MA, Richter CD, Cha KH. Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:686-696. [PMID: 31622238 PMCID: PMC6812655 DOI: 10.1109/tmi.2018.2870343] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage transfer learning approach that utilized data from similar auxiliary domains for intermediate-stage fine-tuning. Breast imaging data from DBT, digitized screen-film mammography, and digital mammography totaling 4039 unique regions of interest (1797 malignant and 2242 benign) were collected. Using cross validation, we selected the best transfer network from six transfer networks by varying the level up to which the convolutional layers were frozen. In a single-stage transfer learning approach, knowledge from CNN trained on the ImageNet data was fine-tuned directly with the DBT data. In a multi-stage transfer learning approach, knowledge learned from ImageNet was first fine-tuned with the mammography data and then fine-tuned with the DBT data. Two transfer networks were compared for the second-stage transfer learning by freezing most of the CNN structures versus freezing only the first convolutional layer. We studied the dependence of the classification performance on training sample size for various transfer learning and fine-tuning schemes by varying the training data from 1% to 100% of the available sets. The area under the receiver operating characteristic curve (AUC) was used as a performance measure. The view-based AUC on the test set for single-stage transfer learning was 0.85 ± 0.05 and improved significantly (p <; 0.05$ ) to 0.91 ± 0.03 for multi-stage learning. This paper demonstrated that, when the training sample size from the target domain is limited, an additional stage of transfer learning using data from a similar auxiliary domain is advantageous.
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Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 2019; 69:127-157. [PMID: 30720861 PMCID: PMC6403009 DOI: 10.3322/caac.21552] [Citation(s) in RCA: 627] [Impact Index Per Article: 125.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
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Affiliation(s)
- Wenya Linda Bi
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Ahmed Hosny
- Research Scientist, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Matthew B. Schabath
- Associate Member, Department of Cancer EpidemiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Maryellen L. Giger
- Professor of Radiology, Department of RadiologyUniversity of ChicagoChicagoIL
| | - Nicolai J. Birkbak
- Research Associate, The Francis Crick InstituteLondonUnited Kingdom
- Research Associate, University College London Cancer InstituteLondonUnited Kingdom
| | - Alireza Mehrtash
- Research Assistant, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Research Assistant, Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverBCCanada
| | - Tavis Allison
- Research Assistant, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Research Assistant, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Omar Arnaout
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Christopher Abbosh
- Research Fellow, The Francis Crick InstituteLondonUnited Kingdom
- Research Fellow, University College London Cancer InstituteLondonUnited Kingdom
| | - Ian F. Dunn
- Associate Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Raymond H. Mak
- Associate Professor, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Rulla M. Tamimi
- Associate Professor, Department of MedicineBrigham and Women’s Hospital, Dana‐Farber Cancer Institute, Harvard Medical SchoolBostonMA
| | - Clare M. Tempany
- Professor of Radiology, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Charles Swanton
- Professor, The Francis Crick InstituteLondonUnited Kingdom
- Professor, University College London Cancer InstituteLondonUnited Kingdom
| | - Udo Hoffmann
- Professor of Radiology, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA
| | - Lawrence H. Schwartz
- Professor of Radiology, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Chair, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Robert J. Gillies
- Professor of Radiology, Department of Cancer PhysiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Raymond Y. Huang
- Assistant Professor, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Hugo J. W. L. Aerts
- Associate Professor, Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Professor in AI in Medicine, Radiology and Nuclear Medicine, GROWMaastricht University Medical Centre (MUMC+)MaastrichtThe Netherlands
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Zhang F, Wu S, Zhang C, Chen Q, Yang X, Jiang K, Zheng J. Multi-domain features for reducing false positives in automated detection of clustered microcalcifications in digital breast tomosynthesis. Med Phys 2019; 46:1300-1308. [PMID: 30661242 DOI: 10.1002/mp.13394] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 12/26/2018] [Accepted: 01/03/2019] [Indexed: 01/04/2023] Open
Abstract
PURPOSE In digital breast tomosynthesis (DBT) imaging, a microcalcification (MC) cluster may span across different slices and blurring exists in the out-of-focus slices. We developed a radiomics approach to extract features from focus slice and combine multiple spatial domains to reduce false positives (FPs) in an automated pipeline of detecting MC clusters. METHODS We performed a retrospective study on a cohort of 290 Chinese women patients with a total of 580 DBT volumes. We developed an automated MC detection pipeline that consists of two stages: an initial detection to identify a set of MC candidates that may include many FPs, followed by a radiomics-based classification model to identify and reduce the FPs. We extract both two-dimensional (2D) and three-dimensional (3D) radiomics features from multiple spatial domains, including a focus slice, projection image, and tomographic volume. A linear discriminant classifier was used coupled with a sequential forward feature selection procedure. The free-response operating characteristics (FROC) curve and partial area under the FROC curve (pAUC) in the FP rate range of 0 to 2 per DBT volume were used to evaluate the model's performance. RESULTS At a sensitivity of 90%, the FP rate was reduced from 1.3 to 0.2 per DBT volume after applying the multi-domain-based classification on the initial detections. The multi-domain yielded a significantly higher pAUC compared to the initial detection (increase of pAUC = 0.2278, P < 0.0001), focus slice (increase of pAUC = 0.0345, P = 0.0152), project image (increase of pAUC = 0.1043, P < 0.0001), and tomographic volume (increase of pAUC = 0.0791, P = 0.0032). CONCLUSION The radiomic features extracted from the three domains may provide complementary information and their integration can significantly reduce FPs in automated detection of MCs in DBT volumes on a large Chinese women population.
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Affiliation(s)
- Fan Zhang
- University of Science and Technology of China, Hefei, 230026, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Shandong Wu
- Departments of Radiology, Biomedical Informatics, Bioengineering, Intelligent Systems, and Computer Science, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Cheng Zhang
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Qian Chen
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215000, China
| | - Xiaodong Yang
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Ke Jiang
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215000, China
| | - Jian Zheng
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
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Horvat JV, Keating DM, Rodrigues-Duarte H, Morris EA, Mango VL. Calcifications at Digital Breast Tomosynthesis: Imaging Features and Biopsy Techniques. Radiographics 2019; 39:307-318. [PMID: 30681901 DOI: 10.1148/rg.2019180124] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Full-field digital mammography (FFDM), the standard of care for breast cancer screening, has some limitations. With the advent of digital breast tomosynthesis (DBT), improvements including decreased recall rates and increased cancer detection rates have been observed. The quasi-three-dimensional capability of DBT reduces breast tissue overlap, a significant limitation of FFDM. However, early studies demonstrate that a few cancers detected at FFDM may not be diagnosed at DBT-only screening, and lesions with calcifications as the dominant feature may look less suspicious at DBT or not be visible at all. These findings support the use of combined FFDM and DBT protocols to optimize screening performance. However, this combination would approximately double the patient's radiation exposure. The development of computer algorithms that generate two-dimensional synthesized mammography (SM) views from DBT has improved calcification conspicuity and sensitivity. Therefore, SM may substitute for FFDM in screening protocols, reducing radiation exposure. DBT plus SM demonstrates significantly better performance than that of FFDM alone, although there are reports of missed malignant calcifications. Thus, some centers continue to perform FFDM with DBT. Use of DBT in breast imaging has also necessitated the development of DBT-guided biopsy. DBT-guided biopsy may have a higher success rate than that of stereotactic biopsy, with a shorter procedure time. While DBT brings substantial improvements to breast cancer imaging, it is important to be aware of its strengths and limitations regarding detection of calcifications. This article reviews the imaging appearance of breast calcifications at DBT, discusses calcification biopsy techniques, and provides an overview of the current literature. Online supplemental material is available for this article. ©RSNA, 2019 An earlier incorrect version of this article appeared online. This article was corrected on February 13, 2019.
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Affiliation(s)
- Joao V Horvat
- From the Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, Suite 715, New York, NY 10065
| | - Delia M Keating
- From the Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, Suite 715, New York, NY 10065
| | - Halio Rodrigues-Duarte
- From the Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, Suite 715, New York, NY 10065
| | - Elizabeth A Morris
- From the Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, Suite 715, New York, NY 10065
| | - Victoria L Mango
- From the Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, Suite 715, New York, NY 10065
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Katzen J, Dodelzon K. A review of computer aided detection in mammography. Clin Imaging 2018; 52:305-309. [PMID: 30216858 DOI: 10.1016/j.clinimag.2018.08.014] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 08/13/2018] [Accepted: 08/16/2018] [Indexed: 01/23/2023]
Abstract
Breast screening with mammography is widely recognized as the most effective method of detecting early breast cancer and has consistently demonstrated a 20-40% decrease in mortality among screened women. Despite this, the sensitivity of mammography ranges between 70 and 90%. Computer aided detection (CAD) is an artificial intelligence (AI) technique that utilizes pattern recognition to highlight suspicious features on imaging and marks them for the radiologist to review and interpret. It aims to decrease oversights made by interpreting radiologists. Here we review the efficacy of CAD and potential future directions.
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Affiliation(s)
- Janine Katzen
- Department of Radiology, Weill Cornell Medicine, 425 E 61st Street, New York, NY 10065, United States of America.
| | - Katerina Dodelzon
- Department of Radiology, Weill Cornell Medicine, 425 E 61st Street, New York, NY 10065, United States of America
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Lu Y, Chan HP, Wei J, Hadjiiski LM, Samala RK. Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction. Phys Med Biol 2017; 62:7765-7783. [PMID: 28832336 DOI: 10.1088/1361-6560/aa8803] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In digital breast tomosynthesis (DBT), the high-attenuation metallic clips marking a previous biopsy site in the breast cause errors in the estimation of attenuation along the ray paths intersecting the markers during reconstruction, which result in interplane and inplane artifacts obscuring the visibility of subtle lesions. We proposed a new metal artifact reduction (MAR) method to improve image quality. Our method uses automatic detection and segmentation to generate a marker location map for each projection (PV). A voting technique based on the geometric correlation among different PVs is designed to reduce false positives (FPs) and to label the pixels on the PVs and the voxels in the imaged volume that represent the location and shape of the markers. An iterative diffusion method replaces the labeled pixels on the PVs with estimated tissue intensity from the neighboring regions while preserving the original pixel values in the neighboring regions. The inpainted PVs are then used for DBT reconstruction. The markers are repainted on the reconstructed DBT slices for radiologists' information. The MAR method is independent of reconstruction techniques or acquisition geometry. For the training set, the method achieved 100% success rate with one FP in 19 views. For the test set, the success rate by view was 97.2% for core biopsy microclips and 66.7% for clusters of large post-lumpectomy markers with a total of 10 FPs in 58 views. All FPs were large dense benign calcifications that also generated artifacts if they were not corrected by MAR. For the views with successful detection, the metal artifacts were reduced to a level that was not visually apparent in the reconstructed slices. The visibility of breast lesions obscured by the reconstruction artifacts from the metallic markers was restored.
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Simplified computer-aided detection scheme of microcalcification clusters in digital breast tomosynthesis images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1070-1073. [PMID: 28268510 DOI: 10.1109/embc.2016.7590888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A computer-aided detection (CADe) algorithm for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) images is suggested. The MC-like objects were enhanced by a Hessian-based 3D calcification response function, and a signal-to-noise ratio (SNR) enhanced image was also generated to screen the MC clustering seed objects. A connected component segmentation method was used to detect the cluster seed objects, which were considered as potential clustering centers of MCs. Bounding cubes for the accepted clustering seed candidate were generated and the overlapping cubes were combined and examined. After the MC clustering and false-positive (FP) reduction step, the average number of FPs was estimated to be 0.87 per DBT volume with a sensitivity of 90.5%.
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Samala RK, Chan HP, Hadjiiski LM, Helvie MA. Analysis of computer-aided detection techniques and signal characteristics for clustered microcalcifications on digital mammography and digital breast tomosynthesis. Phys Med Biol 2016; 61:7092-7112. [PMID: 27648708 DOI: 10.1088/0031-9155/61/19/7092] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
With IRB approval, digital breast tomosynthesis (DBT) images of human subjects were collected using a GE GEN2 DBT prototype system. Corresponding digital mammograms (DMs) of the same subjects were collected retrospectively from patient files. The data set contained a total of 237 views of DBT and equal number of DM views from 120 human subjects, each included 163 views with microcalcification clusters (MCs) and 74 views without MCs. The data set was separated into training and independent test sets. The pre-processing, object prescreening and segmentation, false positive reduction and clustering strategies for MC detection by three computer-aided detection (CADe) systems designed for DM, DBT, and a planar projection image generated from DBT were analyzed. Receiver operating characteristic (ROC) curves based on features extracted from microcalcifications and free-response ROC (FROC) curves based on scores from MCs were used to quantify the performance of the systems. Jackknife FROC (JAFROC) and non-parametric analysis methods were used to determine the statistical difference between the FROC curves. The difference between the CADDM and CADDBT systems when the false positive rate was estimated from cases without MCs did not reach statistical significance. The study indicates that the large search space in DBT may not be a limiting factor for CADe to achieve similar performance as that observed in DM.
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Affiliation(s)
- Ravi K Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109-5842, USA
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Three-Dimensional Computer-Aided Detection of Microcalcification Clusters in Digital Breast Tomosynthesis. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8651573. [PMID: 27274993 PMCID: PMC4870350 DOI: 10.1155/2016/8651573] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 02/12/2016] [Accepted: 02/17/2016] [Indexed: 11/17/2022]
Abstract
We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.
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Vedantham S, Karellas A, Vijayaraghavan GR, Kopans DB. Digital Breast Tomosynthesis: State of the Art. Radiology 2016; 277:663-84. [PMID: 26599926 DOI: 10.1148/radiol.2015141303] [Citation(s) in RCA: 126] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
This topical review on digital breast tomosynthesis (DBT) is provided with the intent of describing the state of the art in terms of technology, results from recent clinical studies, advanced applications, and ongoing efforts to develop multimodality imaging systems that include DBT. Particular emphasis is placed on clinical studies. The observations of increase in cancer detection rates, particularly for invasive cancers, and the reduction in false-positive rates with DBT in prospective trials indicate its benefit for breast cancer screening. Retrospective multireader multicase studies show either noninferiority or superiority of DBT compared with mammography. Methods to curtail radiation dose are of importance. (©) RSNA, 2015.
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Affiliation(s)
- Srinivasan Vedantham
- From the Department of Radiology, University of Massachusetts Medical School, 55 Lake Ave North, Worcester, MA 01655 (S.V., A.K., G.R.V.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.B.K.)
| | - Andrew Karellas
- From the Department of Radiology, University of Massachusetts Medical School, 55 Lake Ave North, Worcester, MA 01655 (S.V., A.K., G.R.V.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.B.K.)
| | - Gopal R Vijayaraghavan
- From the Department of Radiology, University of Massachusetts Medical School, 55 Lake Ave North, Worcester, MA 01655 (S.V., A.K., G.R.V.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.B.K.)
| | - Daniel B Kopans
- From the Department of Radiology, University of Massachusetts Medical School, 55 Lake Ave North, Worcester, MA 01655 (S.V., A.K., G.R.V.); and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (D.B.K.)
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Pike R, Sechopoulos I, Fei B. A minimum spanning forest based classification method for dedicated breast CT images. Med Phys 2015; 42:6190-202. [PMID: 26520712 DOI: 10.1118/1.4931958] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. METHODS Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors' classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. RESULTS Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. CONCLUSIONS A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.
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Affiliation(s)
- Robert Pike
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329
| | - Ioannis Sechopoulos
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329 and Winship Cancer Institute of Emory University, Atlanta, Georgia 30322
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322; Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia 30322; and Winship Cancer Institute of Emory University, Atlanta, Georgia 30322
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Samala RK, Chan HP, Lu Y, Hadjiiski LM, Wei J, Helvie MA. Computer-aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images. Phys Med Biol 2015; 60:8457-79. [PMID: 26464355 DOI: 10.1088/0031-9155/60/21/8457] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We propose a novel approach for the detection of microcalcification clusters (MCs) using joint information from digital breast tomosynthesis (DBT) volume and planar projection (PPJ) image. A data set of 307 DBT views was collected with IRB approval using a prototype DBT system. The system acquires 21 projection views (PVs) from a wide tomographic angle of 60° (60°-21PV) at about twice the dose of a digital mammography (DM) system, which allows us the flexibility of simulating other DBT acquisition geometries using a subset of the PVs. In this study, we simulated a 30° DBT geometry using the central 11 PVs (30°-11PV). The narrower tomographic angle is closer to DBT geometries commercially available or under development and the dose is matched approximately to that of a DM. We developed a new joint-CAD system for detection of clustered microcalcifications. The DBT volume was reconstructed with a multiscale bilateral filtering regularized method and a PPJ image was generated from the reconstructed volume. Task-specific detection strategies were designed to combine information from the DBT volume and the PPJ image. The data set was divided into a training set (127 views with MCs) and an independent test set (104 views with MCs and 76 views without MCs). The joint-CAD system outperformed the individual CAD systems for DBT volume or PPJ image alone; the differences in the test performances were statistically significant (p < 0.05) using JAFROC analysis.
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Affiliation(s)
- Ravi K Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109842, USA
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Morra L, Sacchetto D, Durando M, Agliozzo S, Carbonaro LA, Delsanto S, Pesce B, Persano D, Mariscotti G, Marra V, Fonio P, Bert A. Breast Cancer: Computer-aided Detection with Digital Breast Tomosynthesis. Radiology 2015; 277:56-63. [PMID: 25961633 DOI: 10.1148/radiol.2015141959] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To evaluate a commercial tomosynthesis computer-aided detection (CAD) system in an independent, multicenter dataset. MATERIALS AND METHODS Diagnostic and screening tomosynthesis mammographic examinations (n = 175; cranial caudal and mediolateral oblique) were randomly selected from a previous institutional review board-approved trial. All subjects gave informed consent. Examinations were performed in three centers and included 123 patients, with 132 biopsy-proven screening-detected cancers, and 52 examinations with negative results at 1-year follow-up. One hundred eleven lesions were masses and/or microcalcifications (72 masses, 22 microcalcifications, 17 masses with microcalcifications) and 21 were architectural distortions. Lesions were annotated by radiologists who were aware of all available reports. CAD performance was assessed as per-lesion sensitivity and false-positive results per volume in patients with negative results. RESULTS Use of the CAD system showed per-lesion sensitivity of 89% (99 of 111; 95% confidence interval: 81%, 94%), with 2.7 ± 1.8 false-positive rate per view, 62 of 72 lesions detected were masses, 20 of 22 were microcalcification clusters, and 17 of 17 were masses with microcalcifications. Overall, 37 of 39 microcalcification clusters (95% sensitivity, 95% confidence interval: 81%, 99%) and 79 of 89 masses (89% sensitivity, 95% confidence interval: 80%, 94%) were detected with the CAD system. On average, 0.5 false-positive rate per view were microcalcification clusters, 2.1 were masses, and 0.1 were masses and microcalcifications. CONCLUSION A digital breast tomosynthesis CAD system can allow detection of a large percentage (89%, 99 of 111) of breast cancers manifesting as masses and microcalcification clusters, with an acceptable false-positive rate (2.7 per breast view). Further studies with larger datasets acquired with equipment from multiple vendors are needed to replicate the findings and to study the interaction of radiologists and CAD systems.
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Affiliation(s)
- Lia Morra
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Daniela Sacchetto
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Manuela Durando
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Silvano Agliozzo
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Luca Alessandro Carbonaro
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Silvia Delsanto
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Barbara Pesce
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Diego Persano
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Giovanna Mariscotti
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Vincenzo Marra
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Paolo Fonio
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
| | - Alberto Bert
- From the Department of Research and Development, im3D, Via Lessolo 3, 10153 Turin, Italy (L.M., D.S., S.A., S.D., D.P., A.B.); Department of Radiology, University of Turin, Turin, Italy (M.D., G.M., P.F.); Department of Diagnostic Imaging and Radiation Therapy, Radiology University of Torino, Azienda Ospedaliero Universitaria Città della Salute e della Scienza di Torino, Turin, Italy (M.D., G.M., P.F.); Unità di Radiologia, IRCCS Policlinico S. Donato, Milan, Italy (L.C.); C.d.C. Paideia, Rome, Italy (B.P.); and Department of Radiology, Sant'Anna Hospital, Turin, Italy (V.M.)
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Wei J, Chan HP, Hadjiiski LM, Helvie MA, Lu Y, Zhou C, Samala R. Multichannel response analysis on 2D projection views for detection of clustered microcalcifications in digital breast tomosynthesis. Med Phys 2014; 41:041913. [PMID: 24694144 DOI: 10.1118/1.4868694] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
PURPOSE To investigate the feasibility of a new two-dimensional (2D) multichannel response (MCR) analysis approach for the detection of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT). METHODS With IRB approval and informed consent, a data set of two-view DBTs from 42 breasts containing biopsy-proven MC clusters was collected in this study. The authors developed a 2D approach for MC detection using projection view (PV) images rather than the reconstructed three-dimensional (3D) DBT volume. Signal-to-noise ratio (SNR) enhancement processing was first applied to each PV to enhance the potential MCs. The locations of MC candidates were then identified with iterative thresholding. The individual MCs were decomposed with Hermite-Gaussian (HG) and Laguerre-Gaussian (LG) basis functions and the channelized Hotelling model was trained to produce the MCRs for each MC on the 2D images. The MCRs from the PVs were fused in 3D by a coincidence counting method that backprojects the MC candidates on the PVs and traces the coincidence of their ray paths in 3D. The 3D MCR was used to differentiate the true MCs from false positives (FPs). Finally a dynamic clustering method was used to identify the potential MC clusters in the DBT volume based on the fact that true MCs of clinical significance appear in clusters. Using two-fold cross validation, the performance of the 3D MCR for classification of true and false MCs was estimated by the area under the receiver operating characteristic (ROC) curve and the overall performance of the MCR approach for detection of clustered MCs was assessed by free response receiver operating characteristic (FROC) analysis. RESULTS When the HG basis function was used for MCR analysis, the detection of MC cluster achieved case-based test sensitivities of 80% and 90% at the average FP rates of 0.65 and 1.55 FPs per DBT volume, respectively. With LG basis function, the average FP rates were 0.62 and 1.57 per DBT volume at the same sensitivity levels. The difference in the two sets of basis functions for detection of MCs did not show statistical significance. CONCLUSIONS The authors' experimental results indicate that the MCR approach is promising for the detection of MCs on PV images. The HG or LG basis functions are both effective in characterizing the signal response of MCs using the channelized Hotelling model. The coincidence counting method for fusion of the 2D MCR in 3D is an important step for FP reduction. Further study is underway to improve the MCR approach for microcalcification detection in DBT.
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Affiliation(s)
- Jun Wei
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Lubomir M Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Mark A Helvie
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Yao Lu
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Chuan Zhou
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Ravi Samala
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
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Samala RK, Chan HP, Lu Y, Hadjiiski LM, Wei J, Helvie MA. Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images. Phys Med Biol 2014; 59:7457-77. [PMID: 25393654 DOI: 10.1088/0031-9155/59/23/7457] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This paper describes a new approach to detect microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) via its planar projection (PPJ) image. With IRB approval, two-view (cranio-caudal and mediolateral oblique views) DBTs of human subject breasts were obtained with a GE GEN2 prototype DBT system that acquires 21 projection angles spanning 60° in 3° increments. A data set of 307 volumes (154 human subjects) was divided by case into independent training (127 with MCs) and test sets (104 with MCs and 76 free of MCs). A simultaneous algebraic reconstruction technique with multiscale bilateral filtering (MSBF) regularization was used to enhance microcalcifications and suppress noise. During the MSBF regularized reconstruction, the DBT volume was separated into high frequency (HF) and low frequency components representing microcalcifications and larger structures. At the final iteration, maximum intensity projection was applied to the regularized HF volume to generate a PPJ image that contained MCs with increased contrast-to-noise ratio (CNR) and reduced search space. High CNR objects in the PPJ image were extracted and labeled as microcalcification candidates. Convolution neural network trained to recognize the image pattern of microcalcifications was used to classify the candidates into true calcifications and tissue structures and artifacts. The remaining microcalcification candidates were grouped into MCs by dynamic conditional clustering based on adaptive CNR threshold and radial distance criteria. False positive (FP) clusters were further reduced using the number of candidates in a cluster, CNR and size of microcalcification candidates. At 85% sensitivity an FP rate of 0.71 and 0.54 was achieved for view- and case-based sensitivity, respectively, compared to 2.16 and 0.85 achieved in DBT. The improvement was significant (p-value = 0.003) by JAFROC analysis.
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Affiliation(s)
- Ravi K Samala
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109-5842, USA
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Mansour S, Adel L, Mokhtar O, Omar OS. Comparative study between breast tomosynthesis and classic digital mammography in the evaluation of different breast lesions. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2014. [DOI: 10.1016/j.ejrnm.2014.03.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Chan HP, Goodsitt MM, Helvie MA, Zelakiewicz S, Schmitz A, Noroozian M, Paramagul C, Roubidoux MA, Nees AV, Neal CH, Carson P, Lu Y, Hadjiiski L, Wei J. Digital breast tomosynthesis: observer performance of clustered microcalcification detection on breast phantom images acquired with an experimental system using variable scan angles, angular increments, and number of projection views. Radiology 2014; 273:675-85. [PMID: 25007048 DOI: 10.1148/radiol.14132722] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To investigate the dependence of microcalcification cluster detectability on tomographic scan angle, angular increment, and number of projection views acquired at digital breast tomosynthesis ( DBT digital breast tomosynthesis ). MATERIALS AND METHODS A prototype DBT digital breast tomosynthesis system operated in step-and-shoot mode was used to image breast phantoms. Four 5-cm-thick phantoms embedded with 81 simulated microcalcification clusters of three speck sizes (subtle, medium, and obvious) were imaged by using a rhodium target and rhodium filter with 29 kV, 50 mAs, and seven acquisition protocols. Fixed angular increments were used in four protocols (denoted as scan angle, angular increment, and number of projection views, respectively: 16°, 1°, and 17; 24°, 3°, and nine; 30°, 3°, and 11; and 60°, 3°, and 21), and variable increments were used in three (40°, variable, and 13; 40°, variable, and 15; and 60°, variable, and 21). The reconstructed DBT digital breast tomosynthesis images were interpreted by six radiologists who located the microcalcification clusters and rated their conspicuity. RESULTS The mean sensitivity for detection of subtle clusters ranged from 80% (22.5 of 28) to 96% (26.8 of 28) for the seven DBT digital breast tomosynthesis protocols; the highest sensitivity was achieved with the 16°, 1°, and 17 protocol (96%), but the difference was significant only for the 60°, 3°, and 21 protocol (80%, P < .002) and did not reach significance for the other five protocols (P = .01-.15). The mean sensitivity for detection of medium and obvious clusters ranged from 97% (28.2 of 29) to 100% (24 of 24), but the differences fell short of significance (P = .08 to >.99). The conspicuity of subtle and medium clusters with the 16°, 1°, and 17 protocol was rated higher than those with other protocols; the differences were significant for subtle clusters with the 24°, 3°, and nine protocol and for medium clusters with 24°, 3°, and nine; 30°, 3°, and 11; 60°, 3° and 21; and 60°, variable, and 21 protocols (P < .002). CONCLUSION With imaging that did not include x-ray source motion or patient motion during acquisition of the projection views, narrow-angle DBT digital breast tomosynthesis provided higher sensitivity and conspicuity than wide-angle DBT digital breast tomosynthesis for subtle microcalcification clusters.
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Affiliation(s)
- Heang-Ping Chan
- From the Department of Radiology, University of Michigan Medical Center, 1500 E Medical Center Dr, Ann Arbor, MI 48109-5842 (H.P.C., M.M.G., M.A.H., M.N., C.P., M.A.R., A.V.N., C.H.N., P.C., Y.L., L.H., J.W.); and GE Global Research, Niskayuna, NY (S.Z., A.S.)
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Samala RK, Chan HP, Lu Y, Hadjiiski L, Wei J, Sahiner B, Helvie MA. Computer-aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume. Med Phys 2014; 41:021901. [PMID: 24506622 PMCID: PMC3977832 DOI: 10.1118/1.4860955] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Revised: 12/18/2013] [Accepted: 12/18/2013] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Develop a computer-aided detection (CADe) system for clustered microcalcifications in digital breast tomosynthesis (DBT) volume enhanced with multiscale bilateral filtering (MSBF) regularization. METHODS With Institutional Review Board approval and written informed consent, two-view DBT of 154 breasts, of which 116 had biopsy-proven microcalcification (MC) clusters and 38 were free of MCs, was imaged with a General Electric GEN2 prototype DBT system. The DBT volumes were reconstructed with MSBF-regularized simultaneous algebraic reconstruction technique (SART) that was designed to enhance MCs and reduce background noise while preserving the quality of other tissue structures. The contrast-to-noise ratio (CNR) of MCs was further improved with enhancement-modulated calcification response (EMCR) preprocessing, which combined multiscale Hessian response to enhance MCs by shape and bandpass filtering to remove the low-frequency structured background. MC candidates were then located in the EMCR volume using iterative thresholding and segmented by adaptive region growing. Two sets of potential MC objects, cluster centroid objects and MC seed objects, were generated and the CNR of each object was calculated. The number of candidates in each set was controlled based on the breast volume. Dynamic clustering around the centroid objects grouped the MC candidates to form clusters. Adaptive criteria were designed to reduce false positive (FP) clusters based on the size, CNR values and the number of MCs in the cluster, cluster shape, and cluster based maximum intensity projection. Free-response receiver operating characteristic (FROC) and jackknife alternative FROC (JAFROC) analyses were used to assess the performance and compare with that of a previous study. RESULTS Unpaired two-tailed t-test showed a significant increase (p < 0.0001) in the ratio of CNRs for MCs with and without MSBF regularization compared to similar ratios for FPs. For view-based detection, a sensitivity of 85% was achieved at an FP rate of 2.16 per DBT volume. For case-based detection, a sensitivity of 85% was achieved at an FP rate of 0.85 per DBT volume. JAFROC analysis showed a significant improvement in the performance of the current CADe system compared to that of our previous system (p = 0.003). CONCLUSIONS MBSF regularized SART reconstruction enhances MCs. The enhancement in the signals, in combination with properly designed adaptive threshold criteria, effective MC feature analysis, and false positive reduction techniques, leads to a significant improvement in the detection of clustered MCs in DBT.
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Affiliation(s)
- Ravi K Samala
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Yao Lu
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Jun Wei
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
| | - Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Maryland 20993
| | - Mark A Helvie
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-5842
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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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Sechopoulos I. A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications. Med Phys 2013; 40:014302. [PMID: 23298127 PMCID: PMC3548896 DOI: 10.1118/1.4770281] [Citation(s) in RCA: 113] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Revised: 11/16/2012] [Accepted: 11/16/2012] [Indexed: 02/03/2023] Open
Abstract
Many important post-acquisition aspects of breast tomosynthesis imaging can impact its clinical performance. Chief among them is the reconstruction algorithm that generates the representation of the three-dimensional breast volume from the acquired projections. But even after reconstruction, additional processes, such as artifact reduction algorithms, computer aided detection and diagnosis, among others, can also impact the performance of breast tomosynthesis in the clinical realm. In this two part paper, a review of breast tomosynthesis research is performed, with an emphasis on its medical physics aspects. In the companion paper, the first part of this review, the research performed relevant to the image acquisition process is examined. This second part will review the research on the post-acquisition aspects, including reconstruction, image processing, and analysis, as well as the advanced applications being investigated for breast tomosynthesis.
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Affiliation(s)
- Ioannis Sechopoulos
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
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