Avaliação da caracterização de lesões em mamografia com recurso a sistemas CAD (Diagnóstico Assistido por Computador): sistemas CAD em mamografia

Provas públicas apresentadas à Escola Superior de Saúde Dr. Lopes Dias do Instituto Politécnico de Castelo Branco para o cumprimento dos requisitos necessários à obtenção do título de Especialista.Os sistemas Computer-Aided Detection/Diagnosis (CAD) auxiliam os médicos na deteção e diferenciação de lesões benignas e malignas, aumentando a performance no diagnóstico do cancro da mama. As lesões da mama estão fortemente correlacionadas com a forma do contorno: lesões benignas apresentam contornos regulares, enquanto as lesões malignas tendem a apresentar contornos irregulares. Desta forma, neste estudo, em primeiro lugar, foram utilizadas medidas quantitativas baseadas na dimensão fractal (DF), as quais podem ajudar na caracterização dos contornos regulares ou irregulares das lesões encontradas nas imagens de mamografia, sendo calculadas através da aplicação do método “box-counting”, diretamente em imagens de lesões segmentadas e após a aplicação de um algoritmo de dilatação/erosão. Seguidamente, foi testada a aplicação de Redes Neuronais de Convolução (CNN), as quais têm sido recentemente utilizadas, com elevado grau de sucesso, na deteção e classificação de patologias em diferentes modalidades de imagem médica, incluindo a mamografia. Uma vez que as bases de dados de mamografia disponíveis apresentam um número reduzido de amostras, existe uma forma de ultrapassar esta limitação através do uso do método de “transfer learning”, no qual uma rede previamente treinada num grande conjunto de imagens comuns, é posteriormente ajustada para ser aplicada a uma base de dados com imagens de mamografia. Foram utilizados três modelos CNN pré-treinados, tendo sido avaliada a sua performance na classificação de lesões em imagens antes e após a aplicação de um processo de normalização. Também foi avaliada a performance de uma Support Vector Machine (SVM) com a utilização de características extraídas das CNN isoladamente ou combinadas com “handcrafted features”. Os resultados obtidos são encorajadores e podem ajudar a melhorar o diagnóstico automático de lesões em mamografia.ABSTRACT: Computer‑Aided Systems can assist differentiation and classification of breast benign and malignant lesions enhanced the performance of breast cancer diagnosis. Breast lesions are strongly correlated with their shape: benign lesions present regular shape, although malignant lesions tend to present irregular shape. Therefore, in this work, firstly, were used quantitative measures based on fractal dimension (FD), which can help characterizing the smoothness or the roughness of mammographic lesion shape, being calculated through the box‑counting method, directly from manually segmented lesions, and after applying a region growing/erosion algorithm. Then, Convolutional neural networks (CNNs) application were tested, which have recently been successfully used in the medical field to detect and classify pathologies in different imaging modalities, including in mammography. Since the mammographic databases have a restricted number of samples, one way to solve this problem is using a transfer learning approach, in which a CNN, previously pre-trained with a large amount of labelled non-medical data, is subsequently finetuned using a smaller dataset of mammographic images. Were applied three different pre-trained networks and evaluated their performance to classify lesions in mammograms is affected by the use, or not, of normalized images. Was also evaluated the performance of a support vector machine (SVM) fed with features extracted from the CNN and the combined use of handcrafted features to complement the CNN-extracted features. The obtained results are encouraging and can help to improve computer-aided diagnostic of mammographic lesions.info:eu-repo/semantics/publishedVersio

The Influence of Image Normalization in Mammographic Classification with CNNs

In order to improve the performance of Convolutional Neural Networks (CNN) in the classification of mammographic images, many researchers choose to apply a normalization method during the pre-processing stage. In this work, we aim to assess the impact of six different normalization methods in the classification performance of two CNNs. Results allow us to concluded that the effect of image normalization in the performance of the CNNs depends of which network is chosen to make the lesion classification; besides, the normalization method that seems to have the most positive impact is the one that subtracts the image mean and divide it by the corresponding standard deviation (best AUC mean with CNN-F = 0.786 and with Caffe = 0.790; best run AUC result was 0.793 with CNN-F and 0.791 with Caffe).info:eu-repo/semantics/publishedVersio

Avaliação da caracterização de lesões em mamografia com recurso a sistemas CAD (Diagnóstico Assistido por Computador)

Mestrado em Radiações Aplicadas às Tecnologias da Saúde. Área de especialização: Imagem Digital com Radiação X.Os sistemas Computer-Aided Diagnosis (CAD) auxiliam a deteção e diferenciação de lesões benignas e malignas, melhorando o diagnóstico do cancro da mama. Podem, por isso, ajudar a ultrapassar muitos dos fatores que eventualmente prejudicam a capacidade de deteção de lesões por parte dos clínicos como sejam a incerteza, a fadiga ocular, questões ambientais como a iluminação, a falta de qualidade das imagens, falta de estudos comparativos e, em alguns casos, a falta de experiência do médico especialista. O principal objetivo deste trabalho é verificar se a utilização concomitante da medida da dimensão fractal (DF) tradicional (a qual designamos por DF de contorno) com uma nova medida da dimensão fractal por nós proposta (a qual designamos por DF de área), ambas calculadas através do método box-counting, após a aplicação prévia, ou não, de algoritmos de dilatação/erosão, pode auxiliar a classificação das lesões mamográficas de acordo com a escala BIRADS e permitir diferenciação destas entre lesões benignas e malignas. Os resultados obtidos durante este trabalho indicam que a DF de contorno constitui, uma medida útil na classificação lesões de acordo com as diferentes categorias da escala BIRADS; no entanto, a utilização combinada das medidas de DF propostas pode auxiliar a classificação, evitando desse modo a ocorrência de erros. No que diz respeito à diferenciação das lesões, a DF de contorno fornece bons resultados, sobretudo no caso lesões benignas, microcalcificações e áreas de distorção ou densificações irregulares no caso de lesões mamárias malignas (BIRADS 4 e 5). Neste caso, a DF de área pode ser utilizada em complementaridade, de forma a auxiliar na diferenciação das lesões uma vez que, por si só não apresenta resultados suficientemente satisfatórios. - ABSTRACT - Computer-Aided Diagnosis (CAD) systems can assist the detection and the differentiation of benign and malignant lesions, increasing the performance of breast cancer diagnosis. They help overcoming the main confounding factors that may hinder the detection by clinicians. The main purpose of this work is to assess if the concomitant use of the traditional fractal dimension (FD) measure (which we call contour FD) with a new proposed measure of fractal dimension (which we call area FD), both computed by box-counting method, after the previous application, or not, of region growing/erosion algorithms, can improve the differentiation of benign and malignant lesions. To accomplish this goal, each mammogram in our database was segmented and analyzed using the box-counting method, in order to obtain the different FD measures of lesions. Results indicate that the “traditional” contour FD is a useful measure in the differentiation of lesions according to the BIRADS scale and type, although, in some situations, errors occur. The combined use of contour FD with the four proposed FD measures can improve the classification of lesions according to the BIRADS scale. Results also indicate that the differentiation of lesions relatively to their type can also be improved by the combined use of contour and area FD measures

Lesion Classification in Mammograms Using Convolutional Neural Networks and Transfer Learning

Computer-Aided Detection/Diagnosis (CAD) tools were created to assist the detection and diagnosis of early stage cancers, decreasing false negative rate and improving radiologists’ efficiency. Convolutional Neural Networks (CNNs) are one example of deep learning algorithms that proved to be successful in image classification. In this paper we aim to study the application of CNNs to the classification of lesions in mammograms. One major problem in the training of CNNs for medical applications is the large dataset of images that is often required but seldom available. To solve this problem, we use a transfer learning approach, wich is based on three different networks that were pre-trained on the Imagenet dataset. We then investigate the performance of these pre-trained CNNs and two types of image normalization to classify lesions in mammograms. The best results were obtained using the Caffe reference model for the CNN with no image normalization.info:eu-repo/semantics/publishedVersio

Assessment of mammographic lesions characterization with CAD (Computer-Aided Diagnosis) systems

Computer-Aided Systems can assist differentiation and classification of breast benign and malignant lesions enhanced the performance of breast cancer diagnosis. Breast lesions are strongly correlated with their shape: benign lesions present regular shape, although malignant lesions tend to present irregular shape. Therefore, the use of quantitative measures, such as fractal dimension (FD), can help characterizing the smoothness or the roughness of lesion shape. The main purpose of this work is to assess if the concomitant use of FD measure (contour FD) with a proposed FD measure (area FD) can improve the classification of lesions according to the BIRADS (Breast Imaging Reporting and Data System) scale and lesion type. Both FD measures were calculated through the box-counting method, directly from manually segmented lesions, and after applying a region growing/erosion algorithm. The last FD measure is based on the normalized difference between the FD measures before and after application of region growing/erosion algorithm. Results indicate that the contour FD is a useful measure in the differentiation of lesions according to the BIRADS scale and type, although, in some situations, errors occur. The combined use of contour FD with the four proposed FD measures can improve the classification of lesions

Image Normalization Influence in Mammographic Classification with CNNs

In order to improve the performance of Convolutional Neural Networks (CNN) in the classification of mammographic images, many researchers choose to apply a normalization method during the pre-processing stage. In this work, we aimed to assess the impact of 6 different normalization methods in the classification performance of 2 CNNs. We have also explored 5 classifiers, being the first one the CNN itself. The other 4 correspond to Support Vector Machine (SVM), Random Forest (RF), Simple Logistic (SL) and Voted Perceptron (VP) classifiers, all of them fed with features extracted from one of the layers - comprised between the sixteenth and the nineteenth - of the CNN. The last 3 classifiers were tested with different options for data testing presentation, according to theWeka software: Supplied Test Set (STS), 10-fold Cross Validation (10-FCV) and Percentage Split (PS). Results indicate that the effect of image normalization in the performance of the CNNs depends on which network is chosen to make the classification; besides, the normalization method that seems to have the most positive impact is the one that subtracts to each image the corresponding image mean and divide it by the standard deviation (best AUC mean values were 0.786 for CNN-F and 0.790 for Caffe; the best run AUC values were, respectively, 0.793 and 0.791. Layer 1 freezing decreased the running time and did not harm the classification performance. Regarding the different classifiers, CNNs used alone with softmax yielded the best results, with the exception of the RF and SL classifiers, both using the 10-FCV and PS options; however, with these options, we cannot guarantee that the test set images are presented for the first time to the network.info:eu-repo/semantics/publishedVersio