Towards improved breast mass detection using dual-view mammogram matching.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

Background: Automatic detection and classification of breast masses in mammograms are still challenging tasks. Today, computer-aided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. Materials and Methods: For an efficient mass diagnosis in mammograms, we proposed an automatic scheme to perform both mass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median filtering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classified into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with a mean of 6.4 ± 4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.

YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms

Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The performance of the algorithm was evaluated on a dataset created especially for the purposes of this study, with 320 images from 80 patients (two time points and two views of each breast) with precisely annotated mass locations by two radiologists. Ninety-six features were extracted and ten classifiers were tested in a leave-one-patient-out and k-fold cross-validation process. Using Neural Networks, the detection of masses was 99.9% accurate. The classification accuracy of the masses as benign or suspicious increased from 92.6%, using the state-of-the-art temporal analysis, to 98%, using the proposed methodology. The improvement was statistically significant (p-value < 0.05). These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the diagnosis of breast masses. Clinical and Translational Impact Statement: The proposed algorithm has the potential to substantially contribute to the development of automated breast cancer Computer-Aided Diagnosis systems with significant impact on patient prognosis.

Although the computer-aided diagnosis (CAD) system can be applied for classifying the breast masses, the effects of this method on improvement of the radiologist' accuracy for distinguishing malignant from benign lesions still remain unclear. This study provided a novel method to classify breast masses by integrating the intelligence of human and machine. In this research, 224 breast masses were selected in mammography from database of DDSM with Breast Imaging Reporting and Data System (BI-RADS) categories. Three observers (a senior and a junior radiologist, as well as a radiology resident) were employed to independently read and classify these masses utilizing the Positive Predictive Values (PPV) for each BI-RADS category. Meanwhile, a CAD system was also implemented for classification of these breast masses between malignant and benign. To combine the decisions from the radiologists and CAD, the fusion method of the Multi-Agent was provided. Significant improvements are observed for the fusion system over solely radiologist or CAD. The area under the receiver operating characteristic curve (AUC) of the fusion system increased by 9.6%, 10.3% and 21% compared to that of radiologists with senior, junior and resident level, respectively. In addition, the AUC of this method based on the fusion of each radiologist and CAD are 3.5%, 3.6% and 3.3% higher than that of CAD alone. Finally, the fusion of the three radiologists with CAD achieved AUC value of 0.957, which was 5.6% larger compared to CAD. Our results indicated that the proposed fusion method has better performance than radiologist or CAD alone.

Purpose: Evaluation of the performance of a computer-aided diagnosis (CAD) system based on the quantified color distribution in strain elastography imaging to evaluate the malignancy of breast tumors. Methods: The database consisted of 31 malignant and 52 benign lesions. A radiologist who was blinded to the diagnosis performed the visual analysis of the lesions. After six months with no eye contact on the breast images, the same radiologist and other two radiologists manually drew the contour of the lesions in B-mode ultrasound, which was masked in the elastography image. In order to measure the amount of hard tissue in a lesion, we developed a CAD system able to identify the amount of hard tissue, represented by red color, and quantify its predominance in a lesion, allowing classification as soft, intermediate, or hard. The data obtained with the CAD system were compared with the visual analysis. We calculated the sensitivity, specificity, and area under the curve (AUC) for the classification using the CAD system from the manual delineation of the contour by each radiologist. Results: The performance of the CAD system for the most experienced radiologist achieved sensitivity of 70.97%, specificity of 88.46%, and AUC of 0.853. The system presented better performance compared with his visual diagnosis, whose sensitivity, specificity, and AUC were 61.29%, 88.46%, and 0.829, respectively. The system obtained sensitivity, specificity, and AUC of 67.70%, 84.60%, and 0.783, respectively, for images segmented by Radiologist 2, and 51.60%, 92.30%, and 0.771, respectively, for those segmented by the Resident. The intra-class correlation coefficient was 0.748. The inter-observer agreement of the CAD system with the different contours was good in all comparisons. Conclusions: The proposed CAD system can improve the radiologist performance for classifying breast masses, with excellent inter-observer agreement. It could be a promising tool for clinical use.

Background: The computer-aided diagnosis (CAD) has been applied in multiple studies conducted recently in order to perform ultrasound (US) of the breast. There are several studies that have indicated that CAD is useful for improving the diagnostic performance in less experienced radiologists. However, there is no study on several readers analyzing the same lesions using a breast phantom and rare reliability studies. Objectives: To investigate the reliability of different readers using computer aided diagnosis (CAD) system in US for determining identical lesions in a breast phantom. Patients and Methods: From March 2016 to February 2017, six readers (three senior and three junior residents in the department of radiology) evaluated and analyzed breast phantom including 14 lesions. At the first line study, three senior residents (3rd grade with more than one month of training for breast ultrasound [US]) and three junior residents (1st grade without breast US training) evaluated and analyzed the US and applied CAD for lesions in breast phantom, and they were able to make final decisions by subjective combination. A month later, they conducted the second line study as they did the first line study. We analyzed the inter- and intra-reader reliability and accuracy of US, CAD, and combinations (subjective, conjunctive, and disjunctive). Results: In the total of first and second line studies of six readers, the kappa value of US (0.609) was significantly higher than CAD (0.411). In the subjective combined conclusion, the kappa value was improved by the junior group. In the whole inter- and intra-reader analysis, the kappa values of final assessment of the senior and junior group were more variable on CAD than on US, especially in the junior group, and this result was statistically significant. The area under the curve (AUC) of US, CAD, subjective, conjunctive, and disjunctive combination in seniors were all better than those of juniors. In all groups, the AUCs, sensitivities, and specificities were improved on conjunctive combined US with CAD. Conclusion: The combination US with CAD improved the reliability and diagnostic performance. The CAD results of the junior group were variable and inconsistent. Therefore, minimum training and experience for breast US is indispensable for the better use of breast CAD, and, combination US with CAD is useful for all readers.

Computer-aided diagnosis (CAD) system is helpful for lesion detection. In this study, we proposed a new mass detection method with analysis of bilateral mammograms. First of all, the mass candidates were detected in single view. To utilize the information in dual view, we match corresponding regions in mediolateral oblique (MLO) and craniocaudally (CC) views of the breast. In this paper, we introduced twin support vector machines (TWSVM) as classifier for mass detection, and proposed a new method for feature selection called multiple twin support vector machines (MTWSVM-RFE) to improve the accuracy of detection.

We conducted an observer performance study using receiver operating characteristic (ROC) methodology to evaluate the effects of computer-aided diagnosis (CAD) on radiologists' performance for characterization of masses on serial mammograms. The automated CAD system, previously developed in our laboratory, can classify masses as malignant or benign based on interval change information on serial mammograms. In this study, 126 temporal image pairs (73 malignant and 53 benign) from 52 patients containing masses on serial mammograms were used. The corresponding masses on each temporal pair were identified by an experienced radiologist and automatically segmented by the CAD program. Morphological, texture, and spiculation features of the mass on the current and the prior mammograms were extracted. The individual features and the difference between the corresponding current and prior features formed a multidimensional feature space. A subset of the most effective features that contained the current, prior, and interval change information was selected by a stepwise procedure and used as input predictor variables to a linear discriminant classifier in a leave-one-case-out training and testing resampling scheme. The linear discriminant classifier estimated the relative likelihood of malignancy of each mass. The classifier achieved a test Az value of 0.87. For the ROC study, 4 MQSA radiologists and 1 breast imaging fellow assessed the masses on the temporal pairs and provided estimates of the likelihood of malignancy without and with CAD. The average A_z value for the likelihood of malignancy estimated by the radiologists was 0.79 without CAD and improved to 0.87 with CAD. The improvement was statistically significant (p=0.0003). This preliminary result indicated that CAD using interval change analysis can significantly improve radiologists' accuracy in classification of masses and thereby may increase the positive predictive value of mammography.

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

To develop an ultrasound computer-aided diagnosis (CAD) system using speckle features of automated breast ultrasound (ABUS) images. The ABUS images of 147 pathologically proven breast masses (76 benign and 71 malignant cases) were used. For each mass, a volume of interest (VOI) was cropped to define the tumor area, and the average number of speckle pixels within a VOI was calculated. In addition, first-order and second-order statistical analyses of the speckle pixels were used to quantify the information of gray-level distributions and the spatial relations among the pixels. Receiver operating characteristic curve analysis was used to evaluate the performance. The proposed CAD system based on speckle patterns achieved an accuracy of 84.4% (124∕147), a sensitivity of 83.1% (59∕71), a specificity of 85.5% (65∕76), and an Az of 0.91. The performance indices of the speckle features were comparable to the performance indices of the morphological features, which include shape and ellipse-fitting features (p-value > 0.05). Furthermore, combining speckle and morphological features yielded an Az that was significantly better than the Az of the morphological features alone (0.96 vs 0.91, p-value = 0.0154). The results suggest that the proposed speckle features, while combined with morphological features, are promising for the classification of breast masses detected using ABUS.

We have previously evaluated the effects of computer-aided diagnosis (CAD) on radiologists' characterization of malignant and benign breast masses in single-view serial mammograms. In this study, we conducted observer performance experiments with ROC methodology in which the radiologists read the serial mammograms in two-views (CC and MLO) without and with CAD. 47 temporal pairs of two-view serial mammograms (27 malignant and 20 benign) containing masses were chosen from 39 patient files and digitized. The corresponding masses on each temporal pair were analyzed by the CAD program. For this data set, the computer classifier achieved a test Az value of 0.90. Five MQSA radiologists assessed the two-view temporal pairs and provided estimates of the likelihood of malignancy without and then with CAD. For the five radiologists, the average Az was 0.81 (range: 0.72-0.88) without CAD and improved to 0.88 (range: 0.86-0.90) with CAD. The improvement was statistically significant (p=0.038). In comparison, the test Az value of the computer classifier for single view analysis was 0.87. The average Az of the radiologists for reading the single view temporal pairs without CAD was 0.78 (range: 0.73-0.83) and was improved significantly (p=0.002) to 0.84 (range: 0.77-0.88) with CAD. CAD using interval change analysis can significantly improve radiologists' accuracy in classification of masses. Classification based on information from two-views is more accurate than that based on single view for both the radiologists and the computer classifier. CAD can further improve radiologists' performance even in two-view reading.

To investigate the value of computer-aided diagnosis (CAD) system in assessing thyroid nodules concurrent with Hashimoto's thyroiditis (HT). Totally 148 patients with 193 thyroid nodules were enrolled. A radiologist assessed the nodules using a thyroid ultrasound CAD system. Additionally, the nodules were evaluated by one experienced radiologist alone, and one training radiologist without and with CAD assistance. The diagnostic performance was compared between the CAD system and the experienced radiologist, and the training radiologist without and with CAD assistance. The CAD system demonstrated a similar sensitivity to that of the experienced radiologist in diagnosing thyroid cancers (89.8% versus 92.4%, P > .05). The specificity and accuracy of the CAD system were lower than that of the experienced radiologist in assessing the nodules with diffusedly altered glands (specificity, 60.0% versus 81.7%, P=.007; accuracy, 77.5% versus 88.1%, P=.011). With CAD assistance, the training radiologist had improved sensitivity and accuracy that increased to 87.9% and 86.8% in classifying nodules with sonographically evident HT (both P=.012). The CAD system has comparable sensitivity, but lower specificity compared with the experienced radiologist in diagnosing thyroid malignancies concurrent with HT. For a radiologist with less experience, the CAD system can help improve the diagnostic performance by increasing sensitivity and accuracy in assessing thyroid nodules with diffusely altered parenchyma.

Use of output from a computerized analysis of a breast image by radiologists may help them in the tasks of detection or diagnostic, and potentially improve the overall interpretation of breast images and the subsequent patient care. Many factors motivate the attempts to aid or automate radiological diagnosis. Inadequacies in interpretation performance may be due to the presence of image noise or normal anatomical structure as well as to known limitations in the human search and perception process. Developments in breast CAD have led to clinically‐used detection systems in screening mammography and preclinical classification systems for diagnostic breast imaging. Diagnostic breast CAD systems provide output for the characterization of lesions on special‐view mammography, breast sonography, and breast MRI in order to aid in patient management decisions, such as biopsy decisions. As imaging continues to expand in the digital era, computer‐aided diagnosis (CAD) may become an integrated tool in the online diagnostic workup of suspect breast lesions using multi‐modality images and advanced PACS. This presentation reviews research in computerized analysis of mammographic, sonographic, and magnetic resonance breast images for detection and diagnosis. It will include the characterization of lesions and the estimation of the probability of malignancy for use in the diagnostic workup of suspect lesions. CAD systems in diagnostic workup usually involve having the computer extract the margin of the lesion from the surrounding parenchyma, extract characteristics (features) of the lesions, merge these computer‐extracted features into an estimate of the probability of malignancy, and as an option, retrieve automatically similar lesions from an online reference library. The aim of CAD in diagnostic workup is to increase classification sensitivity and specificity as well as to reduce intra‐and inter‐observer variability. While the breadth and depth of CAD is increasing, continued and expanded efforts are needed for collecting and confirming databases, establishing methods for evaluation, integrating effectively and efficiently with PACS and RIS systems, and providing means for clinical evaluation.Learning objectives:1. To appreciate the development of CAD in multi‐modality imaging for detection and diagnosis of breast cancer.2. To understand the benefits and challenges as CAD moves into the digital era and is integrated with PACS and HIS.3. To recognize the necessary steps for advancing and integrating CAD clinically.COI: Maryellen Giger is a shareholder and consultant, and receives research funding from R2 Technology, Inc.

To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' characterization of masses on serial mammograms. Two hundred fifty-three temporal image pairs (138 malignant and 115 benign) obtained from 96 patients who had masses on serial mammograms were evaluated. The temporal pairs were formed by matching masses of the same view from two different examinations. Eight radiologists and two breast imaging fellows assessed the temporal pairs with and without computer aid. The classification of accuracy was quantified by using the area under receiver operating characteristic curve (A(z)). The statistical significance of the difference in A(z) between the different reading conditions was estimated with the Dorfman-Berbaum-Metz method for analysis of multireader multicase data and with the Student paired t test for analysis of observer-specific paired data. The average A(z) for radiologists' estimates of the likelihood of malignancy was 0.79 without CAD and improved to 0.84 with CAD. The improvement was statistically significant (P =.005). The corresponding average partial area index was 0.25 without CAD and improved to 0.37 with CAD. The improvement was also statistically significant (P =.005). On the basis of Breast Imaging Reporting and Data System assessments, it was estimated that with CAD, each radiologist, on average, reduced 0.7% (0.8 of 115) of unnecessary biopsies and correctly recommended 5.7% (7.8 of 138) of additional biopsies. CAD based on analysis of interval changes can significantly increase radiologists' accuracy in classification of masses and thereby may be useful in improving correct biopsy recommendations.