Performance of computer-aided detection applied to full-field digital mammography in detection of breast cancers

Improving mammography interpretation for both novice and experienced readers: a comparative study of two commercial artificial intelligence software

OBJECTIVES

To evaluate the improvement of mammography interpretation for novice and experienced radiologists assisted by two commercial AI software.

METHODS

We compared the performance of two AI software (AI-1 and AI-2) in two experienced and two novice readers for 200 mammographic examinations (80 cancer cases). Two reading sessions were conducted within 4 weeks. The readers rated the likelihood of malignancy (range, 1-7) and the percentage probability of malignancy (range, 0-100%), with and without AI assistance. Differences in AUROC, sensitivity, and specificity were analyzed.

RESULTS

Mean AUROC increased in both novice (0.86 to 0.90 with AI-1 [p = 0.005]; 0.91 with AI-2 [p < 0.001]) and experienced readers (0.87 to 0.92 with AI-1 [p < 0.001]; 0.90 with AI-2 [p = 0.004]). Sensitivities increased from 81.3 to 88.8% with AI-1 (p = 0.027) and to 91.3% with AI-2 (p = 0.005) in novice readers, and from 81.9 to 90.6% with AI-1 (p = 0.001) and to 87.5% with AI-2 (p = 0.016) in experienced readers. Specificity did not decrease significantly in both novice (p > 0.999, both) and experienced readers (p > 0.999 with AI-1 and 0.282 with AI-2). There was no significant difference in the performance change depending on the type of AI software (p > 0.999).

CONCLUSION

Commercial AI software improved the diagnostic performance of both novice and experienced readers. The type of AI software used did not significantly impact performance changes. Further validation with a larger number of cases and readers is needed.

CLINICAL RELEVANCE STATEMENT

Commercial AI software effectively aided mammography interpretation irrespective of the experience level of human readers.

KEY POINTS

• Mammography interpretation remains challenging and is subject to a wide range of interobserver variability. • In this multi-reader study, two commercial AI software improved the sensitivity of mammography interpretation by both novice and experienced readers. The type of AI software used did not significantly impact performance changes. • Commercial AI software may effectively support mammography interpretation irrespective of the experience level of human readers.

Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images

Breast cancer has emerged as the most life-threatening disease among women around the world. Early detection and treatment of breast cancer are thought to reduce the need for surgery and boost the survival rate. The Magnetic Resonance Imaging (MRI) segmentation techniques for breast cancer diagnosis are investigated in this article. Kapur's entropy-based multilevel thresholding is used in this study to determine optimal values for breast DCE-MRI lesion segmentation using Gorilla Troops Optimization (GTO). An improved GTO, is developed by incorporating Rotational opposition based-learning (RBL) into GTO called (GTORBL) and applied it to the same problem. The proposed approaches are tested on 20 patients' T2 Weighted Sagittal (T2 WS) DCE-MRI 100 slices. The proposed approaches are compared with Tunicate Swarm Algorithm (TSA), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), Slime Mould Algorithm (SMA), Multi-verse Optimization (MVO), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF). The Dice Similarity Coefficient (DSC), sensitivity, and accuracy of the proposed GTO-based approach is achieved [Formula: see text], [Formula: see text], and [Formula: see text] respectively. Another proposed GTORBL-based segmentation method achieves accuracy values of [Formula: see text] , sensitivity of [Formula: see text] , and DSC of [Formula: see text]. The one-way ANOVA test followed by Tukey HSD and Wilcoxon Signed Rank Test are used to examine the results. Furthermore, Multi-Criteria Decision Making is used to evaluate overall performance focused on sensitivity, accuracy, false-positive rate, precision, specificity, [Formula: see text]-score, Geometric-Mean, and DSC. According to both quantitative and qualitative findings, the proposed strategies outperform other compared methodologies.

A Survey of Convolutional Neural Network in Breast Cancer

Problems

For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers.

Aims

A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD).

Methods

We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation.

Conclusion

Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model's accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography

The present study evaluated the diagnostic performance of artificial intelligence-based computer-aided diagnosis (AI-CAD) compared to that of dedicated breast radiologists in characterizing suspicious microcalcification on mammography. We retrospectively analyzed 435 unilateral mammographies from 420 patients (286 benign; 149 malignant) undergoing biopsy for suspicious microcalcification from June 2003 to November 2019. Commercial AI-CAD was applied to the mammography images, and malignancy scores were calculated. Diagnostic performance was compared between radiologists and AI-CAD using the area under the receiving operator characteristics curve (AUC). The AUCs of radiologists and AI-CAD were not significantly different (0.722 vs. 0.745, p = 0.393). The AUCs of the adjusted category were 0.726, 0.744, and 0.756 with cutoffs of 2%, 10%, and 38.03% for AI-CAD, respectively, which were all significantly higher than those for radiologists alone (all p < 0.05). None of the 27 cases downgraded to category 3 with a cutoff of 2% were confirmed as malignant on pathological analysis, suggesting that unnecessary biopsies could be avoided. Our findings suggest that the diagnostic performance of AI-CAD in characterizing suspicious microcalcification on mammography was similar to that of the radiologists, indicating that it may aid in making clinical decisions regarding the treatment of breast microcalcification.

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

BACKGROUND AND OBJECTIVE

With the recent development in deep learning since 2012, the use of Convolutional Neural Networks (CNNs) in bioinformatics, especially medical imaging, achieved tremendous success. Besides that, breast masses detection and classifications in mammograms and their pathology classification are considered a critical challenge. Till now, the evaluation process of the screening mammograms is held by human readers which is considered very monotonous, tiring, lengthy, costly, and significantly prone to errors.

METHODS

We propose an end to end computer-aided diagnosis system based on You Only Look Once (YOLO). The proposed system first preprocesses the mammograms from their DICOM format to images without losing data. Then, it detects masses in full-field digital mammograms and distinguishes between the malignant and benign lesions without any human intervention. YOLO has three different architectures, and, in this paper, the three versions are used for mass detection and classification in the mammograms to compare their performance. The use of anchors in YOLO-V3 on the original form of data and its augmented version is proved to improve the detection accuracy especially when the k-means clustering is applied to generate anchors corresponding to the used dataset. Finally, ResNet and Inception are used as feature extractors to compare their classification performance against YOLO.

RESULTS

Mammograms with different resolutions are used and based on YOLO-V3, the best results are obtained through detecting 89.4% of the masses in the INbreast mammograms with an average precision of 94.2% and 84.6% for classifying the masses as benign and malignant respectively. YOLO's classification network is replaced with ResNet and InceptionV3 to get overall accuracy of 91.0% and 95.5%, respectively.

CONCLUSION

The proposed system showed using the experimental results the YOLO impact on the breast masses detection and classification. Especially using the anchor boxes concept in YOLO-V3 that are generated by applying k-means clustering on the dataset, we can detect most of the challenging cases of masses and classify them correctly. Also, by augmenting the dataset using different approaches and comparing with other recent YOLO based studies, it is found that augmenting the training set only is the fairest and accurate to be applied in the realistic scenarios.

A tree-based multiclassification of breast tumor histopathology images through deep learning

Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for positive and detailed (at tissue level) diagnosis of breast tumor (BrT) compared to mammogram images. A large number of studies used BrT Hp images to solve binary or multiclassification problems using high computational resources. However, classification models' performance may be compromised due to the high correlation among various types of BrT in Hp images, which raises the misclassification rate. Thus, this paper aims to develop a tree-based BrT multiclassification model via deep learning (DL) to extract discriminative features to solve the multiclassification problem with better performance using less computational resources. The main contributions of this work are to create an ensemble, tree-based DL model that is pre-trained on the BreakHis dataset, and implementation of a misclassification reduction algorithm. The ensemble, tree-based DL model, extracts discriminative BrT features from Hp images. The target dataset (i.e., Bioimaging challenge 2015 breast histology) is small in size; thus, to avoid overfitting of the proposed model, pretraining is performed on the BreakHis dataset. Whereas, misclassification reduction algorithm is implemented to enhance the performance of the classification model. The experimental results show that the proposed model outperformed the existing state-of-the-art baseline studies. The achieved classification accuracy is ranging from 87.50 % to 100 % for four subtypes of BrT. Thus, the proposed model can assist doctors as the second opinion in any healthcare centre.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

A review of computer aided detection in mammography

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.

Detecting and classifying lesions in mammograms with Deep Learning

In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95. The approach described here has achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are published online at https://github.com/riblidezso/frcnn_cad .

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review

BACKGROUND AND OBJECTIVE

The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and classification algorithms. This paper presents the conduction and results of a systematic review (SR) that aims to investigate the state of the art regarding the computer aided diagnosis/detection (CAD) systems for breast cancer.

METHODS

The SR was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The scientific databases used are Springer Link (SL), Science Direct (SD), IEEE Xplore Digital Library, and PubMed. Inclusion and exclusion criteria were defined and applied to each retrieved work to select those of interest. From 320 studies retrieved, 154 studies were included. However, the scope of this research is limited to scientific and academic works and excludes commercial interests.

RESULTS

This survey provides a general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers. Potential research studies have been discussed to create a more objective and efficient CAD systems.

Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer.

A curated mammography data set for use in computer-aided detection and diagnosis research

Published research results are difficult to replicate due to the lack of a standard evaluation data set in the area of decision support systems in mammography; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. This causes an inability to directly compare the performance of methods or to replicate prior results. We seek to resolve this substantial challenge by releasing an updated and standardized version of the Digital Database for Screening Mammography (DDSM) for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Our data set, the CBIS-DDSM (Curated Breast Imaging Subset of DDSM), includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets. The data set contains 753 calcification cases and 891 mass cases, providing a data-set size capable of analyzing decision support systems in mammography.

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification

BACKGROUND AND OBJECTIVE

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential).

METHODS

In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm.

RESULTS

We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature.

CONCLUSIONS

We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features.

Breast Cancer Detection in a Screening Population: Comparison of Digital Mammography, Computer-Aided Detection Applied to Digital Mammography and Breast Ultrasound

Purpose

We aimed to compare the detection of breast cancer using full-field digital mammography (FFDM), FFDM with computer-aided detection (FFDM+CAD), ultrasound (US), and FFDM+CAD plus US (FFDM+CAD+US), and to investigate the factors affecting cancer detection.

Methods

In this retrospective study conducted from 2008 to 2012, 48,251 women underwent FFDM and US for cancer screening. One hundred seventy-one breast cancers were detected: 115 invasive cancers and 56 carcinomas in situ. Two radiologists evaluated the imaging findings of FFDM, FFDM+CAD, and US, based on the Breast Imaging Reporting and Data System lexicon of the American College of Radiology by consensus. We reviewed the clinical and the pathological data to investigate factors affecting cancer detection. We statistically used generalized estimation equations with a logit link to compare the cancer detectability of different imaging modalities. To compare the various factors affecting detection versus nondetection, we used Wilcoxon rank sum, chi-square, or Fisher exact test.

Results

The detectability of breast cancer by US (96.5%) or FFDM+CAD+US (100%) was superior to that of FFDM (87.1%) (p=0.019 or p<0.001, respectively) or FFDM+ CAD (88.3%) (p=0.050 or p<0.001, respectively). However, cancer detectability was not significantly different between FFDM versus FFDM+CAD (p=1.000) and US alone versus FFDM+CAD+US (p=0.126). The tumor size influenced cancer detectability by all imaging modalities (p<0.050). In FFDM and FFDM+CAD, the nondetecting group consisted of younger patients and patients with a denser breast composition (p<0.050). In breast US, carcinoma in situ was more frequent in the nondetecting group (p=0.014).

Conclusion

For breast cancer screening, breast US alone is satisfactory for all age groups, although FFDM+ CAD+US is the perfect screening method. Patient age, breast composition, and pathological tumor size and type may influence cancer detection during screening.

A new region descriptor for multi-modal medical image registration and region detection

Establishing accurate anatomical correspondences plays a critical role in multi-modal medical image registration and region detection. Although many features based registration methods have been proposed to detect these correspondences, they are mostly based on the point descriptor which leads to high memory cost and could not represent local region information. In this paper, we propose a new region descriptor which depicts the features in each region, instead of in each point, as a vector. First, feature attributes of each point are extracted by a Gabor filter bank combined with a gradient filter. Then, the region descriptor is defined as the covariance of feature attributes of each point inside the region, based on which a cost function is constructed for multi-modal image registration. Finally, our proposed region descriptor is applied to both multi-modal region detection and similarity metric measurement in multi-modal image registration. Experiments demonstrate the feasibility and effectiveness of our proposed region descriptor.

Comparison of a discrete wavelet transform method and a modified undecimated discrete wavelet transform method for denoising of mammograms

The purpose of this study was to evaluate the performance of a conventional discrete wavelet transform (DWT) method and a modified undecimated discrete wavelet transform (M-UDWT) method applied to mammographic image denoising. Mutual information, mean square error, and signal to noise ratio were used as image quality measures of images processed by the two methods. We examined the performance of the two methods with visual perceptual evaluation. A two-tailed F test was used to measure statistical significance. The difference between the M-UDWT processed images and the conventional DWT-method processed images was statistically significant (P<0.01). The authors confirmed the superiority and effectiveness of the M-UDWT method. The results of this study suggest the M-UDWT method may provide better image quality as compared to the conventional DWT.

PACS administrators' and radiologists' perspective on the importance of features for PACS selection

Picture archiving and communication systems (PACS) play a critical role in radiology. This paper presents the criteria important to PACS administrators for selecting a PACS. A set of criteria are identified and organized into an integrative hierarchical framework. Survey responses from 48 administrators are used to identify the relative weights of these criteria through an analytical hierarchy process. The five main dimensions for PACS selection in order of importance are system continuity and functionality, system performance and architecture, user interface for workflow management, user interface for image manipulation, and display quality. Among the subdimensions, the highest weights were assessed for security, backup, and continuity; tools for continuous performance monitoring; support for multispecialty images; and voice recognition/transcription. PACS administrators' preferences were generally in line with that of previously reported results for radiologists. Both groups assigned the highest priority to ensuring business continuity and preventing loss of data through features such as security, backup, downtime prevention, and tools for continuous PACS performance monitoring. PACS administrators' next high priorities were support for multispecialty images, image retrieval speeds from short-term and long-term storage, real-time monitoring, and architectural issues of compatibility and integration with other products. Thus, next to ensuring business continuity, administrators' focus was on issues that impact their ability to deliver services and support. On the other hand, radiologists gave high priorities to voice recognition, transcription, and reporting; structured reporting; and convenience and responsiveness in manipulation of images. Thus, radiologists' focus appears to be on issues that may impact their productivity, effort, and accuracy.

Impact of computer-aided detection systems on radiologist accuracy with digital mammography

OBJECTIVE

The purpose of this study was to assess the impact of computer-aided detection (CAD) systems on the performance of radiologists with digital mammograms acquired during the Digital Mammographic Imaging Screening Trial (DMIST).

MATERIALS AND METHODS

Only those DMIST cases with proven cancer status by biopsy or 1-year follow-up that had available digital images were included in this multireader, multicase ROC study. Two commercially available CAD systems for digital mammography were used: iCAD SecondLook, version 1.4; and R2 ImageChecker Cenova, version 1.0. Fourteen radiologists interpreted, without and with CAD, a set of 300 cases (150 cancer, 150 benign or normal) on the iCAD SecondLook system, and 15 radiologists interpreted a different set of 300 cases (150 cancer, 150 benign or normal) on the R2 ImageChecker Cenova system.

RESULTS

The average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI, 0.67-0.77) with the iCAD system (p = 0.07). Similarly, the average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI 0.67-0.77) with the R2 system (p = 0.08). Sensitivity and specificity differences without and with CAD for both systems also were not significant.

CONCLUSION

Radiologists in our studies rarely changed their diagnostic decisions after the addition of CAD. The application of CAD had no statistically significant effect on radiologist AUC, sensitivity, or specificity performance with digital mammograms from DMIST.

A modified undecimated discrete wavelet transform based approach to mammographic image denoising

In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.

Detection of breast cancer with a computer-aided detection applied to full-field digital mammography

A study was conducted to evaluate the sensitivity of computer-aided detection (CAD) with full-field digital mammography in detection of breast cancer, based on mammographic appearance and histopathology. Retrospectively, CAD sensitivity was assessed in total group of 152 cases for subgroups based on breast density, mammographic presentation, lesion size, and results of histopathological examination. The overall sensitivity of CAD was 91 % (139 of 152 cases). CAD detected 100 % (47/47) of cancers manifested as microcalcifications; 98 % (62/63) of those manifested as non-calcified masses; 100 % (15/15) of those manifested as mixed masses and microcalcifications; 75 % (12/16) of those manifested as architectural distortions, and 69 % (18/26) of those manifested as focal asymmetry. CAD sensitivity was 83 % (10/12) for cancers measuring 1-10 mm, 92 % (37/40) for those measuring 11-20 mm, and 92 % (92/100) for those measuring >20 mm. There was no significant difference in CAD detection efficiency between cancers in dense breasts (88 %; 69/78) and those in non-dense breasts (95 %; 70/74). CAD showed a high sensitivity of 91 % (139/152) for the mammographic appearance of cancer and 100 % sensitivity for identifying cancers manifested as microcalcifications. Sensitivity was not influenced by breast density or lesion size. CAD should be effective for helping radiologists detect breast cancer at an earlier stage.

Role of computer-aided detection in very small screening detected invasive breast cancers

This study aims to assess computer-aided detection (CAD) performance with full-field digital mammography (FFDM) in very small (equal to or less than 1 cm) invasive breast cancers. Sixty-eight invasive breast cancers less than or equal to 1 cm were retrospectively studied. All cases were detected with FFDM in women aged 49-69 years from our breast cancer screening program. Radiological characteristics of lesions following BI-RADS descriptors were recorded and compared with CAD sensitivity. Age, size, BI-RADS classification, breast density type, histological type of the neoplasm, and role of the CAD were also assessed. Per-study specificity and mass false-positive rate were determined by using 100 normal consecutive studies. Thirty-seven (54.4 %) masses, 17 (25 %) calcifications, 6 (8.8 %) masses with calcifications, 7 (10.3 %) architectural distortions, and 1 asymmetry (1.5 %) were found. CAD showed an overall sensitivity of 86.7 % (masses, 86.5 %; calcifications, 100 %; masses with calcifications, 100 %; and architectural distortion, 57.14 %), CAD failed to detect 9 out of 68 cases: 5 of 37 masses, 3 of 7 architectural distortions, and 1 of 1 asymmetry. Fifteen out of 37 masses were hyperdense, and all of them were detected by CAD. No association was seen among mass morphology or margins and detectability. Per-study specificity and CAD false-positive rate was 26 % and 1.76 false marks per study. In conclusion, CAD shows a high sensitivity and a low specificity. Lesion size, histology, and breast density do not influence sensitivity. Mammographic features, mass density, and thickness of the spicules in architectural distortions do influence.

A computer-aided diagnostic algorithm improves the accuracy of transesophageal echocardiography for left atrial thrombi: a single-center prospective study

OBJECTIVES

We investigated whether transesophageal echocardiography (TEE) assisted with a computer-aided diagnostic (CAD) algorithm was superior to TEE in diagnosing left atrial (LA)/left atrial appendage (LAA) thrombi in patients with atrial fibrillation (AF) in a single prospective study.

METHODS

Transesophageal echocardiography was performed in patients with AF, and images were reconstructed. Gray level co-occurrence matrix-based features were calculated and then classified using an artificial neural network. The original data and processed images by the CAD system were studied by 5 radiologists independently in a blind manner. The diagnostic performance of each radiologist was evaluated.

RESULTS

One hundred thirty patients with AF were investigated. Thirty-one patients (23.9%) had a diagnosis of LA/LAA thrombi. The mean sensitivity ± SD of TEE for LA/LAA thrombi was 0.933 ± 0.027, which was noticeably improved by CAD (0.955 ± 0.021; P < .05). The specificity of TEE was 0.811 ± 0.055, which was markedly lower than that by TEE plus CAD (0.970 ± 0.009; P < .05). The positive predictive value of TEE was low (0.613 ± 0.073) compared to that of TEE plus CAD (0.908 ± 0.027; P < .001), whereas the negative predictive values were comparable for TEE, CAD, and TEE plus CAD. Diagnosis of an LA/LAA thrombus by TEE plus CAD had a higher accuracy rate (0.966 ± 0.011) than that by TEE (0.840 ± 0.047; P < .01). The mean area under the receiver operating characteristic curve (Az) for TEE was 0.834 ± 0.009 (95% confidence interval [CI], 0.815-0.852), which was markedly lower than the Az for TEE plus CAD (0.932 ± 0.005; 95% CI, 0.921-0.943). The use of CAD significantly improved the Az values for all 5 radiologists (P < .001).

CONCLUSIONS

The CAD algorithm significantly improves the diagnostic accuracy of TEE for LA/LAA thrombi in patients with AF.

Automated diagnosis of mammogram images of breast cancer using discrete wavelet transform and spherical wavelet transform features: a comparative study

Mammograms are one of the most widely used techniques for preliminary screening of breast cancers. There is great demand for early detection and diagnosis of breast cancer using mammograms. Texture based feature extraction techniques are widely used for mammographic image analysis. In specific, wavelets are a popular choice for texture analysis of these images. Though discrete wavelets have been used extensively for this purpose, spherical wavelets have rarely been used for Computer-Aided Diagnosis (CAD) of breast cancer using mammograms. In this work, a comparison of the performance between the features of Discrete Wavelet Transform (DWT) and Spherical Wavelet Transform (SWT) based on the classification results of normal, benign and malignant stage was studied. Classification was performed using Linear Discriminant Classifier (LDC), Quadratic Discriminant Classifier (QDC), Nearest Mean Classifier (NMC), Support Vector Machines (SVM) and Parzen Classifier (ParzenC). We have obtained a maximum classification accuracy of 81.73% for DWT and 88.80% for SWT features using SVM classifier.

Decision support system for breast cancer detection using mammograms

Mammograms are by far one of the most preferred methods of screening for breast cancer. Early detection of breast cancer can improve survival rates to a greater extent. Although the analysis and diagnosis of breast cancer are done by experienced radiologists, there is always the possibility of human error. Interobserver and intraobserver errors occur frequently in the analysis of medical images, given the high variability between every patient. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This article presents a classification pipeline to improve the accuracy of differentiation between normal, benign, and malignant mammograms. Several features based on higher-order spectra, local binary pattern, Laws' texture energy, and discrete wavelet transform were extracted from mammograms. Feature selection techniques based on sequential forward, backward, plus-l-takeaway-r, individual, and branch-and-bound selections using the Mahalanobis distance criterion were used to rank the features and find classification accuracies for combination of several features based on the ranking. Six classifiers were used, namely, decision tree classifier, fisher classifier, linear discriminant classifier, nearest mean classifier, Parzen classifier, and support vector machine classifier. We evaluated our proposed methodology with 300 mammograms obtained from the Digital Database for Screening Mammography and 300 mammograms from the Singapore Anti-Tuberculosis Association CommHealth database. Sensitivity, specificity, and accuracy values were used to compare the performances of the classifiers. Our results show that the decision tree classifier demonstrated an excellent performance compared to other classifiers with classification accuracy, sensitivity, and specificity of 91% for the Digital Database for Screening Mammography database and 96.8% for the Singapore Anti-Tuberculosis Association CommHealth database.

Empirical investigation of radiologists' priorities for PACS selection: an analytical hierarchy process approach

Picture archiving and communication systems (PACS) are being widely adopted in radiology practice. The objective of this study was to find radiologists' perspective on the relative importance of the required features when selecting or developing a PACS. Important features for PACS were identified based on the literature and consultation/interviews with radiologists. These features were categorized and organized into a logical hierarchy consisting of the main dimensions and sub-dimensions. An online survey was conducted to obtain data from 58 radiologists about their relative preferences. Analytical hierarchy process methodology was used to determine the relative priority weights for different dimensions along with the consistency of responses. System continuity and functionality was found to be the most important dimension, followed by system performance and architecture, user interface for workflow management, user interface for image manipulation, and display quality. Among the sub-dimensions, the top two features were: security, backup, and downtime prevention; and voice recognition, transcription, and reporting. Structured reporting was also given very high priority. The results point to the dimensions that can be critical discriminators between different PACS and highlight the importance of faster integration of the emerging developments in radiology into PACS.

Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment

OBJECTIVES

To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database.

METHODS

The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database.

RESULTS

The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups.

CONCLUSION

This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.

Computer-aided detection; the effect of training databases on detection of subtle breast masses

RATIONALE AND OBJECTIVES

Lesion conspicuity is typically highly correlated with visual difficulty for lesion detection, and computer-aided detection (CAD) has been widely used as a "second reader" in mammography. Hence, increasing CAD sensitivity in detecting subtle cancers without increasing false-positive rates is important. The aim of this study was to investigate the effect of training database case selection on CAD performance in detecting low-conspicuity breast masses.

MATERIALS AND METHODS

A full-field digital mammographic image database that included 525 cases depicting malignant masses was randomly partitioned into three subsets. A CAD scheme was applied to detect all initially suspected mass regions and compute region conspicuity. Training samples were iteratively selected from two of the subsets. Four types of training data sets-(1) one including all available true-positive mass regions in the two subsets ("all"), (2) one including 350 randomly selected mass regions ("diverse"), (3) one including 350 high-conspicuity mass regions ("easy"), and (4) one including 350 low-conspicuity mass regions ("difficult")-were assembled. In each training data set, the same number of randomly selected false-positive regions as the true-positives were also included. Two classifiers, an artificial neural network (ANN) and a k-nearest neighbor (KNN) algorithm, were trained using each of the four training data sets and tested on all suspected regions in the remaining data set. Using a threefold cross-validation method, the performance changes of the CAD schemes trained using one of the four training data sets were computed and compared.

RESULTS

CAD initially detected 1025 true-positive mass regions depicted on 507 cases (97% case-based sensitivity) and 9569 false-positive regions (3.5 per image) in the entire database. Using the all training data set, CAD achieved the highest overall performance on the entire testing database. However, CAD detected the highest number of low-conspicuity masses when the difficult training data set was used for training. Results did agree for both ANN-based and KNN-based classifiers in all tests. Compared to the use of the all training data set, the sensitivity of the schemes trained using the difficult data set decreased by 8.6% and 8.4% for the ANN and KNN algorithm on the entire database, respectively, but the detection of low-conspicuity masses increased by 7.1% and 15.1% for the ANN and KNN algorithm at a false-positive rate of 0.3 per image.

CONCLUSIONS

CAD performance depends on the size, diversity, and difficulty level of the training database. To increase CAD sensitivity in detecting subtle cancer, one should increase the fraction of difficult cases in the training database rather than simply increasing the training data set size.