Mammographic density. Measurement of mammographic density

Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations

Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system

Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer Using Deep Learning

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics

Image Processing Algorithms for Digital Mammography: A Pictorial Essay

Digital mammography systems allow manipulation of fine differences in image contrast by means of image processing algorithms. Different display algorithms have advantages and disadvantages for the specific tasks required in breast imaging—diagnosis and screening. Manual intensity windowing can produce digital mammograms very similar to standard screen-film mammograms but is limited by its operator dependence. Histogram-based intensity windowing improves the conspicuity of the lesion edge, but there is loss of detail outside the dense parts of the image. Mixture-model intensity windowing enhances the visibility of lesion borders against the fatty background, but the mixed parenchymal densities abutting the lesion may be lost. Contrast-limited adaptive histogram equalization can also provide subtle edge information but might degrade performance in the screening setting by enhancing the visibility of nuisance information. Unsharp masking enhances the sharpness of the borders of mass lesions, but this algorithm may make even an indistinct mass appear more circumscribed. Peripheral equalization displays lesion details well and preserves the peripheral information in the surrounding breast, but there may be flattening of image contrast in the nonperipheral portions of the image. Trex processing allows visualization of both lesion detail and breast edge information but reduces image contrast

유방 촬영술 영상 자료의 딥러닝 적용을 통한 유방암 위험도 평가 : 유방 치밀도 자동 평가 방법 기반

학위논문(석사)--서울대학교 대학원 :보건대학원 보건학과,2019. 8. 성주헌.Introduction : Mammographic density adjusted for age and body mass index (BMI) is the most predictive marker of breast cancer after familial causes and genetic markers. The aim of this study was to develop deep learning (DL) algorithm to assess mammographic density. Methods : Total 2464 participants (834 cases and 1630 controls) were collected from Asan Medical Center and Samsung Medical Center, Korea. Cranio-caudal view mammographic images were obtained using full-field digital mammography system. Mammographic densities were measured using CUMULUS software. The resulting DL algorithm was tested on a held-out test set of 493 women. Agreement on DL and expert was assessed with correlation coefficient and weighted κ statistics. Risk associations of DL measures were evaluated with area under curve (AUC) and odds per adjusted standard deviation (OPERA). Results : The DL model showed very good agreement with expert for both percent density and dense area (r = 0.94 - 0.96 and κ = 0.89 - 0.91). Risk associations of DL measures were comparable to manual measures of expert. DL measures adjusted for age and BMI showed strong risk associations with breast cancer (OPERA = 1.51 - 1.63 and AUC = 0.61 - 0.64). Conclusions : DL model can be used to measure mammographic density which is a strong risk factor of breast cancer. This study showed the potential of DL algorithm as a mammogram-based risk prediction model in breast cancer screening test.유방 내 유방 실질 조직의 양을 반영하는 유방 밀도는 맘모그램에서 나타나는 밝은 부분으로 정의되며, 유방암의 강력한 위험인자로 널리 알려져 있다. 하지만 유방 밀도는 측정하는데 시간과 비용이 많이 든다는 단점으로 인해 유방암 검진 과정에서 제한적으로 사용돼 왔다. 본 연구의 목적은 유방암 검진에서 유방암 예측 모형에 포함해 활용할 수 있는 딥러닝 기반 유방 밀도 측정치를 개발하는 것이다. 본 연구는 아산 병원과 삼성 서울병원의 유방암 검진 자료로부터 수집된 총 2464 명의 참여자 (환자: 834 명, 대조군 : 1630 명) 를 대상으로 수행되었다. 환자의 경우 병변이 발생한 유방의 반대쪽 유방, 대조군의 경우 임의로 고른 유방을 대상으로 유방 밀도 측정에 5년 이상의 경력을 가진 전문가가 CUMULUS 프로그램을 활용하여 유방 밀도 (치밀 유방 부위, cm2 및 치밀도 백분율, %) 를 측정하였다. 이 전문가 측정치를 훈련 데이터로 하여 완전 합성곱 신경망 (Fully Convolutional Network) 기반 딥러닝 모델을 구축하였고, 이를 테스트 데이터에 대해 적용해 전문가 측정치와의 일치도 및 유방암 예측력을 평가하였다. 딥러닝 모델은 전문가와 높은 일치도 (r = 0.94 - 0.96, weighted κ = 0.89 – 0.91) 를 보였다. 또한 나이와 BMI를 보정한 딥러닝 기반 측정치의 유방암 예측력을 평가한 결과, 딥러닝 모델이 전문가와 비슷한 수준의 예측력을 갖는다는 것을 확인하였다 (전문가, AUC = 0.62 – 0.63, 딥러닝 모델, AUC = 0.61 – 0.64). 본 연구는 딥러닝이 현재의 노동 집약적인 유방 밀도 측정법을 보완할 수 있는 가능성을 보여주었다. 이는 비용-효율적인 방법으로 유방 밀도 측정치를 유방암 예측 모형에 포함시킬 수 있는 기회를 제공한다. 이러한 맘모그램 기반 유방암 위험도 예측 모형이 유방암 검진 과정에 적용된다면 보다 정밀한 유방암 위험도 평가를 통해 효과적으로 유방암 고위험군을 선별할 수 있으며, 고위험군에 대한 맞춤형 예방 전략이 적용된다면 장기적으로 유방암 조기 발견 및 사망률 감소에 기여할 수 있을 것으로 기대한다.1 Introduction 1 2 Materials and Methods 3 2.1 Data collection 3 2.2 Measurement of mammographic density 4 2.3 Development of DL model 6 2.3.1 Establishing ground truth 6 2.3.2 Image preprocessing 6 2.3.3 Establishing DL model 6 2.3.4 Estimation of mammographic density 11 2.4 Statistical methods 14 2.4.1 Agreement statistics 14 2.4.2 Evaluation of risk association 15 3 Results 16 3.1 Characteristics of study participants 16 3.2 Agreement of DL model 17 3.3 Breast cancer risk profiles 21 4 Discussion 24 Bibliography 26 초록 29Maste

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find ‘contour-like’ objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ± 8.5% and 97.5 ± 6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases

A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Modelling the interpretation of digital mammography using high order statistics and deep machine learning

Visual search is an inhomogeneous, yet efficient sampling process accomplished by the saccades and the central (foveal) vision. Areas that attract the central vision have been studied for errors in interpretation of medical images. In this study, we extend existing visual search studies to understand features of areas that receive direct visual attention and elicit a mark by the radiologist (True and False Positive decisions) from those that elicit a mark but were captured by the peripheral vision. We also investigate if there are any differences between these areas and those that are never fixated by radiologists. Extending these investigations, we further explore the possibility of modelling radiologists’ search behavior and their interpretation of mammograms using deep machine learning techniques. We demonstrated that energy profiles of foveated (FC), peripherally fixated (PC), and never fixated (NFC) areas are distinct. It was shown that FCs are selected on the basis of being most informative. Never fixated regions were found to be least informative. Evidences that energy profiles and dwell time of these areas influence radiologists’ decisions (and confidence in such decisions) were also shown. High-order features provided additional information to the radiologists, however their effect on decision (and confidence in such decision) was not significant. We also showed that deep-convolution neural network can successfully be used to model radiologists’ attentional level, decisions and confidence in their decisions. High accuracy and high agreement (between true and predicted values) in such predictions can be achieved in modelling attentional level (accuracy: 0.90, kappa: 0.82) and decisions (accuracy: 0.92, kappa: 0.86) of radiologists. Our results indicated that an ensembled model for radiologist’s search behavior and decision can successfully be built. Convolution networks failed to model missed cancers however

Detecting the “gist” of breast cancer in mammograms three years before localized signs of cancer are visible

Objectives: After a 500 ms presentation, experts can distinguish abnormal mammograms at above chance levels even when only the breast contralateral to the lesion is shown. Here, we show that this signal of abnormality is detectable 3 years before localized signs of cancer become visible. Methods: In 4 prospective studies, 59 expert observers from 3 groups viewed 116–200 bilateral mammograms for 500 ms each. Half of the images were prior exams acquired 3 years prior to onset of visible, actionable cancer and half were normal. Exp. 1D included cases having visible abnormalities. Observers rated likelihood of abnormality on a 0–100 scale and categorized breast density. Performance was measured using receiver operating characteristic analysis. Results: In all three groups, observers could detect abnormal images at above chance levels 3 years prior to visible signs of breast cancer (p < 0.001). The results were not due to specific salient cases nor to breast density. Performance was correlated with expertise quantified by the number of mammographic cases read within a year. In Exp. 1D, with cases having visible actionable pathology included, the full group of readers failed to reliably detect abnormal priors; with the exception of a subgroup of the six most experienced observers. Conclusions: Imaging specialists can detect signals of abnormality in mammograms acquired years before lesions become visible. Detection may depend on expertise acquired by reading large numbers of cases. Advances in knowledge: Global gist signal can serve as imaging risk factor with the potential to identify patients with elevated risk for developing cancer, resulting in improved early cancer diagnosis rates and improved prognosis for females with breast cancer