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BACKGROUND: Cognitive rehabilitation is often delayed in multiple sclerosis (MS). OBJECTIVE: To develop a free and specific cognitive rehabilitation programme for MS patients to be used from early stages that does not interfere with daily living activities. METHODS: MS-line!, cognitive rehabilitation materials consisting of written, manipulative and computer-based materials with difficulty levels developed by a multidisciplinary team. RESULTS: Mathematical, problem-solving and word-based exercises were designed. Physical materials included spatial, coordination and reasoning games. Computer-based material included logic and reasoning, working memory and processing speed games. CONCLUSIONS: Cognitive rehabilitation exercises that are specific for MS patients have been successfully developed.
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Transtornos Cognitivos/reabilitação , Cognição/fisiologia , Transtornos da Memória/reabilitação , Esclerose Múltipla/reabilitação , Transtornos Cognitivos/terapia , Humanos , Transtornos da Memória/terapia , Memória de Curto Prazo/fisiologia , Esclerose Múltipla/psicologia , Testes Neuropsicológicos , Resultado do TratamentoRESUMO
Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density.
Assuntos
Algoritmos , Neoplasias da Mama/diagnóstico por imagem , Mama/patologia , Processamento de Imagem Assistida por Computador , Glândulas Mamárias Humanas/anormalidades , Interpretação de Imagem Radiográfica Assistida por Computador , Idoso , Densidade da Mama , Neoplasias da Mama/patologia , Estudos de Viabilidade , Feminino , Humanos , Estudos Longitudinais , Pessoa de Meia-Idade , Reprodutibilidade dos TestesRESUMO
INTRODUCTION: Time-series analysis of magnetic resonance images (MRI) is of great value for multiple sclerosis (MS) diagnosis and follow-up. In this paper, we present an unsupervised subtraction approach which incorporates multisequence information to deal with the detection of new MS lesions in longitudinal studies. METHODS: The proposed pipeline for detecting new lesions consists of the following steps: skull stripping, bias field correction, histogram matching, registration, white matter masking, image subtraction, automated thresholding, and postprocessing. We also combine the results of PD-w and T2-w images to reduce false positive detections. RESULTS: Experimental tests are performed in 20 MS patients with two temporal studies separated 12 (12M) or 48 (48M) months in time. The pipeline achieves very good performance obtaining an overall sensitivity of 0.83 and 0.77 with a false discovery rate (FDR) of 0.14 and 0.18 for the 12M and 48M datasets, respectively. The most difficult situation for the pipeline is the detection of very small lesions where the obtained sensitivity is lower and the FDR higher. CONCLUSION: Our fully automated approach is robust and accurate, allowing detection of new appearing MS lesions. We believe that the pipeline can be applied to large collections of images and also be easily adapted to monitor other brain pathologies.
Assuntos
Interpretação de Imagem Assistida por Computador , Imageamento por Ressonância Magnética/métodos , Esclerose Múltipla/diagnóstico , Humanos , Estudos LongitudinaisRESUMO
INTRODUCTION: Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. METHODS: Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. RESULTS: This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. CONCLUSION: Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI.
Assuntos
Encéfalo/patologia , Imageamento por Ressonância Magnética/métodos , Esclerose Múltipla/patologia , Reconhecimento Automatizado de Padrão , Meios de Contraste , Progressão da Doença , HumanosRESUMO
Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types. Therefore, we provide connected density clusters taking the spatial information of the breast into account. With the aim of showing the robustness of our approach, the experiments are performed using two different databases: the well-known Mammographic Image Analysis Society digitised database and a new full-field digital database of mammograms from which we have annotations provided by radiologists. Quantitative and qualitative results show that our approach is able to correctly detect dense breasts, segmenting the tissue type accordingly.
Assuntos
Neoplasias da Mama/diagnóstico por imagem , Densitometria/métodos , Modelos Estatísticos , Reconhecimento Automatizado de Padrão/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Algoritmos , Mama/patologia , Neoplasias da Mama/patologia , Feminino , Humanos , Intensificação de Imagem Radiográfica/métodosRESUMO
BACKGROUND AND OBJECTIVE: Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of the lesioned tissue, which can help in the clinical practice to better assess and evaluate the risks of each treatment. METHODS: We propose a deep learning methodology for acute and sub-acute stroke lesion segmentation using multimodal MR imaging. We pre-process the data to facilitate learning features based on the symmetry of brain hemispheres. The issue of class imbalance is tackled using small patches with a balanced training patch sampling strategy and a dynamically weighted loss function. Moreover, a combination of whole patch predictions, using a U-Net based CNN architecture, and high degree of overlapping patches reduces the need for additional post-processing. RESULTS: The proposed method is evaluated using two public datasets from the 2015 Ischemic Stroke Lesion Segmentation challenge (ISLES 2015). These involve the tasks of sub-acute stroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES) from multiple diffusion, perfusion and anatomical MRI modalities. The performance is compared against state-of-the-art methods with a blind online testing set evaluation on each of the challenges. At the time of submitting this manuscript, our approach is the first method in the online rankings for the SISS (DSC=0.59 ± 0.31) and SPES sub-tasks (DSC=0.84 ± 0.10). When compared with the rest of submitted strategies, we achieve top rank performance with a lower Hausdorff distance. CONCLUSIONS: Better segmentation results are obtained by leveraging the anatomy and pathophysiology of acute stroke lesions and using a combined approach to minimize the effects of class imbalance. The same training procedure is used for both tasks, showing the proposed methodology can generalize well enough to deal with different unrelated tasks and imaging modalities without hyper-parameter tuning. In order to promote the reproducibility of our results, a public version of the proposed method has been released to the scientific community.
Assuntos
Redes Neurais de Computação , Acidente Vascular Cerebral , Humanos , Imageamento por Ressonância Magnética , Imagem Multimodal , Reprodutibilidade dos Testes , Acidente Vascular Cerebral/diagnóstico por imagemRESUMO
The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. However, the subtle expression of ischemia in acute CT images has made it hard for automated methods to extract potentially quantifiable information. In this work, we present and evaluate an automated deep learning tool for acute stroke lesion core segmentation from CT and CT perfusion images. For evaluation, the Ischemic Stroke Lesion Segmentation (ISLES) 2018 challenge dataset is used that includes 94 cases for training and 62 for testing. The presented method is an improved version of our workshop challenge approach that was ranked among the workshop challenge finalists. The introduced contributions include a more regularized network training procedure, symmetric modality augmentation and uncertainty filtering. Each of these steps is quantitatively evaluated by cross-validation on the training set. Moreover, our proposal is evaluated against other state-of-the-art methods with a blind testing set evaluation using the challenge website, which maintains an ongoing leaderboard for fair and direct method comparison. The tool reaches competitive performance ranking among the top performing methods of the ISLES 2018 testing leaderboard with an average Dice similarity coefficient of 49%. In the clinical setting, this method can provide an estimate of lesion core size and location without performing time costly magnetic resonance imaging. The presented tool is made publicly available for the research community.
Assuntos
Isquemia Encefálica/diagnóstico por imagem , Aprendizado Profundo , Imageamento Tridimensional , Imageamento por Ressonância Magnética , Acidente Vascular Cerebral/diagnóstico por imagem , Tomografia Computadorizada por Raios X , HumanosRESUMO
The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out. The first is related to the ability of the system to detect masses, and thus, free-response receiver operating characteristic analysis was used, showing that the method is able to give high accuracy at a high specificity (80% detection at 1.40 false positives per image). Second, the ability of the system to highlight the pixels belonging to a mass is studied using receiver operating characteristic analysis, resulting in A(z) = 0.89 +/- 0.04. In addition, the robustness of the approach is demonstrated in an experiment where we used the Digital Database for Screening Mammography database for training and the Mammographic Image Analysis Society database for testing the algorithm.
Assuntos
Neoplasias da Mama/diagnóstico , Mamografia/instrumentação , Mamografia/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Algoritmos , Mama/patologia , Neoplasias da Mama/patologia , Computadores , Bases de Dados Factuais , Reações Falso-Positivas , Humanos , Modelos Estatísticos , Probabilidade , Curva ROC , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.
Assuntos
Carcinoma/diagnóstico , Diagnóstico por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Neoplasias da Próstata/diagnóstico , Humanos , Processamento de Imagem Assistida por Computador/métodos , Masculino , Programas de Rastreamento/métodos , Informática Médica , Gradação de Tumores , Redes Neurais de Computação , Variações Dependentes do Observador , Reprodutibilidade dos Testes , Projetos de Pesquisa , Software , Fatores de TempoRESUMO
Digital mammograms may present an overexposed area in the peripheral part of the breast, which is visually shown as a darker area with lower contrast. This has a direct impact on image quality and affects image visualisation and assessment. This paper presents an automatic method to enhance the overexposed peripheral breast area providing a more homogeneous and improved view of the whole mammogram. The method automatically restores the overexposed area by equalising the image using information from the intensity of non-overexposed neighbour pixels. The correction is based on a multiplicative model and on the computation of the distance map from the breast boundary. A total of 334 digital mammograms were used for evaluation. Mammograms before and after enhancement were evaluated by an expert using visual comparison. In 90.42% of the cases, the enhancement obtained improved visualisation compared to the original image in terms of contrast and detail. Moreover, results show that lesions found in the peripheral area after enhancement presented a more homogeneous intensity distribution. Hence, peripheral enhancement is shown to improve visualisation and will play a role in further development of CAD systems in mammography.
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Mama/patologia , Mamografia/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Algoritmos , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Músculo Esquelético/patologia , Reconhecimento Automatizado de Padrão , Intensificação de Imagem Radiográfica/métodos , Reprodutibilidade dos Testes , SoftwareRESUMO
BACKGROUND: Automatic multiple sclerosis lesion segmentation is a challenging task. An extensive analysis of the most recent techniques indicates an improvement of the results obtained when using prior knowledge and contextual information. NEW METHOD: We present BOOST, a knowledge-based approach to automatically segment multiple sclerosis lesions through a voxel by voxel classification. We used the Gentleboost classifier and a set of features, including contextual features, registered atlas probability maps and an outlier map. RESULTS: Results are computed on a set of 45 cases from three different hospitals (15 of each), obtaining a moderate agreement between the manual annotations and the automatically segmented results. COMPARISON WITH EXISTING METHOD(S): We quantitatively compared our results with three public state-of-the-art approaches obtaining competitive results and a better overlap with manual annotations. Our approach tends to better segment those cases with high lesion load, while cases with small lesion load are more difficult to accurately segment. CONCLUSIONS: We believe BOOST has potential applicability in the clinical practice, although it should be improved in those cases with small lesion load.
Assuntos
Mapeamento Encefálico , Encéfalo/patologia , Modelos Neurológicos , Esclerose Múltipla/patologia , Reconhecimento Automatizado de Padrão , Algoritmos , Feminino , Humanos , Conhecimento , Imageamento por Ressonância Magnética , Masculino , ProbabilidadeRESUMO
Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.
Assuntos
Mapeamento Encefálico/métodos , Imageamento por Ressonância Magnética/métodos , Esclerose Múltipla/diagnóstico , Esclerose Múltipla/patologia , Algoritmos , Automação , Encéfalo/patologia , Bases de Dados Factuais , Humanos , Processamento de Imagem Assistida por Computador/métodos , Distribuição Normal , Probabilidade , Reprodutibilidade dos Testes , SoftwareRESUMO
Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi-resolution framework. The model parameters are then modified with the prior knowledge of the optimization space to achieve optimal prostate segmentation. In contrast to traditional statistical models of shape and intensity priors, we use posterior probabilities of the prostate region determined from random forest classification to build our appearance model, initialize and propagate our model. Furthermore, multiple mean models derived from spectral clustering of combined shape and appearance parameters are applied in parallel to improve segmentation accuracies. The proposed method achieves mean Dice similarity coefficient value of 0.91 ± 0.09 for 126 images containing 40 images from the apex, 40 images from the base and 46 images from central regions in a leave-one-patient-out validation framework. The mean segmentation time of the procedure is 0.67 ± 0.02 s.
Assuntos
Algoritmos , Inteligência Artificial , Interpretação de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Próstata/diagnóstico por imagem , Neoplasias da Próstata/patologia , Ultrassonografia/métodos , Interpretação Estatística de Dados , Humanos , Aumento da Imagem/métodos , Masculino , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented.
Assuntos
Imageamento por Ressonância Magnética/métodos , Próstata/anatomia & histologia , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Humanos , Masculino , Próstata/diagnóstico por imagem , Próstata/patologia , UltrassonografiaRESUMO
PURPOSE: Prostate volume estimation from segmentation of transrectal ultrasound (TRUS) images aids in diagnosis and treatment of prostate hypertrophy and cancer. Computer-aided accurate and computationally efficient prostate segmentation in TRUS images is a challenging task, owing to low signal-to-noise ratio, speckle noise, calcifications, and heterogeneous intensity distribution in the prostate region. METHOD: A multi-resolution framework using texture features in a parametric deformable statistical model of shape and appearance was developed to segment the prostate. Local phase information of log-Gabor quadrature filter extracted texture of the prostate region in TRUS images. Large bandwidth of log-Gabor filter ensures easy estimation of local orientations, and zero response for a constant signal provides invariance to gray level shift. This aids in enhanced representation of the underlying texture information of the prostate unaffected by speckle noise and imaging artifacts. The parametric model of the propagating contour is derived from principal component analysis of prior shape and texture information of the prostate from the training data. The parameters were modified using prior knowledge of the optimization space to achieve segmentation. RESULTS: The proposed method achieves a mean Dice similarity coefficient value of 0.95 ± 0.02 and mean absolute distance of 1.26 ± 0.51 millimeter when validated with 24 TRUS images of 6 data sets in a leave-one-patient-out validation framework. CONCLUSIONS: The proposed method for prostate TRUS image segmentation is computationally efficient and provides accurate prostate segmentations in the presence of intensity heterogeneities and imaging artifacts.
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Processamento de Imagem Assistida por Computador/métodos , Modelos Estatísticos , Reconhecimento Automatizado de Padrão/métodos , Próstata/diagnóstico por imagem , Humanos , Hipertrofia/diagnóstico por imagem , Masculino , Neoplasias da Próstata/diagnóstico por imagem , UltrassonografiaRESUMO
Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.
Assuntos
Encéfalo/anatomia & histologia , Imageamento por Ressonância Magnética/métodos , Humanos , ProbabilidadeRESUMO
The detection of architectural distortions and abnormal structures in mammographic images can be based on the analysis of bilateral and temporal cases using image registration. This paper presents a quantitative evaluation of state-of-the art intensity based image registration methods applied to mammographic images. These methods range from a global and rigid transformation to local deformable paradigms using various metrics and multiresolution approaches. The aim of this study is to assess the suitability of these methods for mammographic image analysis. Evaluation using temporal cases based on quantitative analysis and a multiobserver study is presented which gives an indication of the accuracy and robustness of the different algorithms. Although previous studies suggested that local deformable methods were not suitable due to the generation of unrealistic distortions, in this work we show that local deformable paradigms (multiresolution B-Spline deformations) obtain the most accurate registration results.
Assuntos
Mamografia , Algoritmos , Feminino , HumanosRESUMO
Computer Aided Detection (CAD) mammographic systems are used in medicine to assist radiologists in the evaluation of mammographic images. The aim of this work is to compare the results of a developed single-image CAD system with a new one, dual-image CAD, that adds registration information of bilateral mammographic images in the training step of the former system. The evaluation of the different registration methods is performed using similarity measures. Receiver Operating Characteristic (ROC) analysis and Free Receiver Operating Characteristics (FROC) analysis are used to compare the results of both CAD systems. At a sensitivity of 80%, the false positives per image was 1.68 for the single-image CAD system and 0.90 for the dual-image CAD system. The results shows the benefits of integrating bilateral information into the CAD system.
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Algoritmos , Diagnóstico por Computador/métodos , Feminino , Humanos , Mamografia , Curva ROC , Interpretação de Imagem Radiográfica Assistida por ComputadorRESUMO
The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.
Assuntos
Algoritmos , Inteligência Artificial , Neoplasias da Mama/diagnóstico por imagem , Mamografia/métodos , Reconhecimento Automatizado de Padrão/métodos , Intensificação de Imagem Radiográfica/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Feminino , Humanos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
RATIONALE AND OBJECTIVES: The goal of this article is to analyze and compare the performance of a developed mass computer-aided detection (CAD) system that takes breast density information into account when using manual or automatic breast density annotations in the training step. The advantages of considering this breast density information will be highlighted. MATERIALS AND METHODS: The image database used in this article is 92 mediolateral oblique (MLO) and 92 craniocaudal (CC) mammograms obtained by a full-field digital mammographic unit. All mammograms contain at least one mass. The evaluation of the experiments is performed using free receiver operating characteristic analysis for evaluating the detection performance and pixel-based receiver operating characteristic analysis for evaluating the segmentation accuracy. In addition, the performance of the automatic breast density classifier is shown using confusion matrices. RESULTS: When the breast density information is not considered and at a specificity of two false positives per image, the sensitivity obtained by the CAD system is 0.747 for the CC views and 0.853 for the MLO views. Considering the breast density information, the sensitivity for CC and MLO mammograms increases to 0.800 and 0.893, respectively, using manual classification, and 0.827 and 0.907, respectively, using automatic estimation. The same trend is observed when evaluating the CAD segmentation accuracy for detected masses in terms of area under the curve values: without considering breast density, these are 0.920 +/- 0.057 and 0.917 +/- 0.072; using manual classification, 0.934 +/- 0.039 and 0.932 +/- 0.046; and using automatic estimation, 0.947 +/- 0.038 and 0.946 +/- 0.045 for CC and MLO views, respectively. CONCLUSIONS: The experiments showed improved results when breast density information was taken into account. Moreover, the results obtained when using automatic breast density estimation outperformed those based on the manual annotations provided by expert radiologists. In this sense, the experiments showed that breast density information can be beneficial for CAD systems, and this information can be estimated robustly by an automatic procedure, which reduces the inter- and intra-class variability of the radiologists.