Detecting and classifying lesions in mammograms with Deep Learning

In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms. The benefits of current CAD technologies appear to be contradictory and 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 the 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 availaible online at https://github.com/riblidezso/frcnn_cad

A comprehensive model for x-ray projection imaging system efficiency and image quality characterization in the presence of scattered radiation.

This work proposes a method for assessing the detective quantum efficiency (DQE) of radiographic imaging systems that include both the x-ray detector and the antiscatter device. Cascaded linear analysis of the antiscatter device efficiency (DQEASD) with the x-ray detector DQE is used to develop a metric of system efficiency (DQEsys); the new metric is then related to the existing system efficiency parameters of effective DQE (eDQE) and generalized DQE (gDQE). The effect of scatter on signal transfer was modelled through its point spread function (PSF), leading to an x-ray beam transfer function (BTF) that multiplies with the classical presampling modulation transfer function (MTF) to give the system MTF. Expressions are then derived for the influence of scattered radiation on signal-difference to noise ratio (SDNR) and contrast-detail (c-d) detectability. The DQEsys metric was tested using two digital mammography systems, for eight x-ray beams (four with and four without scatter), matched in terms of effective energy. The model was validated through measurements of contrast, SDNR and MTF for poly(methyl)methacrylate thicknesses covering the range of scatter fractions expected in mammography. The metric also successfully predicted changes in c-d detectability for different scatter conditions. Scatter fractions for the four beams with scatter were established with the beam stop method using an extrapolation function derived from the scatter PSF, and validated through Monte Carlo (MC) simulations. Low-frequency drop of the MTF from scatter was compared to both theory and MC calculations. DQEsys successfully quantified the influence of the grid on SDNR and accurately gave the break-even object thickness at which system efficiency was improved by the grid. The DQEsys metric is proposed as an extension of current detector characterization methods to include a performance evaluation in the presence of scattered radiation, with an antiscatter device in place

A Computer-Based Cascaded Modeling and Experimental Approach to the Physical Characterization of a Clinical Full-Field Mammography System

This study characterizes the image quality parameters of a clinical full-field digital mammography system at various x-ray spectral conditions. The energy of the incident x-ray beam, the spectral characteristics, and breast thickness impact the physical performance such as the detective quantum efficiency of the system, thereby affecting the overall performance. The modulation transfer function, noise power spectrum were measured without the anti-scatter grid, and the detective quantum efficiency was calculated for different incident x-ray conditions. Detective quantum efficiency was also calculated with the anti-scatter grid placed above the detector to study its impact. Results indicate a substantial drop in the detective quantum efficiency with the anti-scatter grid under certain conditions. It was also determined that detective quantum efficiency decreases as x-ray beam hardening is increased. A spatial frequency-dependent cascaded liner systems model was developed to predict the detective quantum efficiency of the system for different target-filter combinations. This theoretical model is based upon a serial cascade approach in which the system is conceptually divided into a number of discrete stages. Each stage represents a physical process having intrinsic signal and noise transfer properties. A match between the predicted data and the experimental detective quantum efficiency data confirmed the validity of the model. Contrast-detail performance, a widely used quality control tool to assess clinical imaging systems, for the clinical full-field digital mammography was studied using a commercially available CDMAM phantom to learn the effects of Joint Photographic Experts Group 2000 (JPEG2000) compression technique on detectability. A 4-alternative forced choice experiment was conducted. The images were compressed at three different compression ratios (10:1, 20:1 and 30:1). From the contrast-detail curves generated from the observer data at 50% and 75% threshold levels, it was concluded that uncompressed images exhibit lower (better) contrast-detail characteristics than compressed images but a certain limit to compression, without substantial loss of visual quality, can be used

Performance of a novel wafer scale CMOS active pixel sensor for bio-medical imaging

Recently CMOS Active Pixels Sensors (APSs) have become a valuable alternative to amorphous Silicon and Selenium Flat Panel Imagers (FPIs) in bio-medical imaging applications. CMOS APSs can now be scaled up to the standard 20 cm diameter wafer size by means of a reticle stitching block process. However despite wafer scale CMOS APS being monolithic, sources of non-uniformity of response and regional variations can persist representing a significant challenge for wafer scale sensor response. Non-uniformity of stitched sensors can arise from a number of factors related to the manufacturing process, including variation of amplification, variation between readout components, wafer defects and process variations across the wafer due to manufacturing processes. This paper reports on an investigation into the spatial non-uniformity and regional variations of a wafer scale stitched CMOS APS. For the first time a per-pixel analysis of the electro-optical performance of a wafer CMOS APS is presented, to address inhomogeneity issues arising from the stitching techniques used to manufacture wafer scale sensors. A complete model of the signal generation in the pixel array has been provided and proved capable of accounting for noise and gain variations across the pixel array. This novel analysis leads to readout noise and conversion gain being evaluated at pixel level, stitching block level and in regions of interest, resulting in a coefficient of variation ≤ 1.9%. The uniformity of the image quality performance has been further investigated in a typical X-ray application, i.e. mammography, showing a uniformity in terms of CNR among the highest when compared with mammography detectors commonly used in clinical practise. Finally, in order to compare the detection capability of this novel APS with the currently used technology (i.e. FPIs), theoretical evaluation of the Detection Quantum Efficiency (DQE) at zero-frequency has been performed, resulting in a higher DQE for this detector compared to FPIs. Optical characterization, X-ray contrast measurements and theoretical DQE evaluation suggest that a trade off can be found between the need of a large imaging area and the requirement of a uniform imaging performance, making the DynAMITe large area CMOS APS suitable for a range of bio-medical applications

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

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

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available. (C) 2014 Elsevier Ireland Ltd. All rights reserved.This work was supported by research grants from Gent per Gent Fund (EDEMAC Project); Spain's Health Research Fund (Fondo de Investigacion Santiaria) (PI060386 & FIS PS09/00790); Spanish MICINN grants TIN2009-14205-C04-02 and Consolider-Ingenio 2010: MIPRCV (CSD2007-00018); Spanish Federation of Breast Cancer Patients (Federacion Espanola de Cancer de Mama) (FECMA 485 EPY 1170-10). The English revision of this paper was funded by the Universitat Politecnica de Valencia, Spain.Llobet Azpitarte, R.; Pollán, M.; Antón Guirao, J.; Miranda-García, J.; Casals El Busto, M.; Martinez Gomez, I.; Ruiz Perales, F.... (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine. 116(2):105-115. https://doi.org/10.1016/j.cmpb.2014.01.021S105115116

Investigation of 3D electrical impedance mammography systems for breast cancer detection

Breast cancer is a major disease in women worldwide with a high rate of mortality, second only to lung cancer. Hence, there is considerable interest in developing non-invasive breast cancer detection methods with the aim of identifying breast cancer at an early stage, when it is most treatable. Electrical impedance mammography (EIM) is a relatively new medical imaging method for breast cancer detection. It is a safe, painless, non-invasive, non-ionizing imaging modality, which visualizes the internal conductivity distribution of the breast under investigation. Currently some EIM systems are in clinical trials but not commercialized, as there are still many challenges with sensitivity, spatial resolution and detectability. The research in this thesis aims to enhance and optimize EIM systems in order to address the current challenges. An enhanced image reconstruction algorithm using the duo-mesh method is developed. Both in simulations and real cases of phantoms and patients, the enhanced algorithm has proven more accurate and sensitive than the former algorithm and effective in improving vertical resolution for the EIM system with a planar electrode array. To evaluate the performance of the EIM system and the image reconstruction algorithms, an image processing based error analysis method is developed, which can provide an intuitive and accurate method to evaluate the reconstructed image and outline the shape of the object of interest. Two novel EIM systems are studied, which aim to improve the spatial resolution and the detectability of a tumour deep in the breast volume. These are: rotary planar-electrode-array EIM (RPEIM) system and combined electrode array EIM (CEIM) system. The RPEIM system permits the planar electrode array to rotate in the horizontal plane, which can dramatically increase the number of independent measurements, hence improving the spatial resolution. To support the rotation of the planner electrode array, a synchronous mesh method is developed. The CEIM system has a planar electrode array and a ring electrode array operated independently or together. It has three operational modes. This design provides enhanced detectability of a tumour deep within the tissue, as required for a large volume breast. The studies of the RPEIM system and the CEIM system are based on close-to-realistic digital breast phantoms, which comprise of skin, nipple, ducts, acini, fat and tumour. This approach makes simulations very close to a clinical trial of the technology