Dalí Freire, Ph.D.

Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is often constrained by the availability of labeled training data since labeling histological images is a highly skilled, complex, and time-consuming task. Thus, data augmentation strategies become essential to train convolutional neural networks models to overcome the overfitting… Mostrar más

Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is often constrained by the availability of labeled training data since labeling histological images is a highly skilled, complex, and time-consuming task. Thus, data augmentation strategies become essential to train convolutional neural networks models to overcome the overfitting problem when only a few training samples are available. This paper proposes a new data augmentation strategy, named Random Composition Augmentation (RCAug), to train fully convolutional networks (FCN) to segment OSCC tumor regions in H &E-stained histological images. Given the input image and their corresponding label, a pipeline with a random composition of geometric, distortion, color transfer, and generative image transformations is executed on the fly. Experimental evaluations were performed using an FCN-based method to segment OSCC regions through a set of different data augmentation transformations. By using RCAug, we improved the FCN-based segmentation method from 0.51 to 0.81 of intersection-over-union (IOU) in a whole slide image dataset and from 0.65 to 0.69 of IOU in a tissue microarray images dataset. Mostrar menos

Computer-aided diagnosis (CAD) can be used as an important tool to aid and enhance pathologists' diagnostic decision-making. Deep learning techniques, such as convolutional neural networks (CNN) and fully convolutional networks (FCN), have been successfully applied in medical and biological research. Unfortunately, histological image segmentation is often constrained by the availability of labeled training data once labeling histological images for segmentation purposes is a highly-skilled… Mostrar más

Computer-aided diagnosis (CAD) can be used as an important tool to aid and enhance pathologists' diagnostic decision-making. Deep learning techniques, such as convolutional neural networks (CNN) and fully convolutional networks (FCN), have been successfully applied in medical and biological research. Unfortunately, histological image segmentation is often constrained by the availability of labeled training data once labeling histological images for segmentation purposes is a highly-skilled, complex, and time-consuming task. This paper presents the hematoxylin and eosin (H&E) stained oral cavity-derived cancer (OCDC) dataset, a labeled dataset containing H&E-stained histological images of oral squamous cell carcinoma (OSCC) cases. The tumor regions in our dataset are labeled manually by a specialist and validated by a pathologist. The OCDC dataset presents 1,020 histological images of size 640x640 pixels containing tumor regions fully annotated for segmentation purposes. All the histological images are digitized at 20x magnification. Mostrar menos

The diagnosis of different types of cancer, including oral cavity-derived cancer, is made by a pathologist through complex and time-consuming microscopic analysis of tissue samples. This paper presents a method based on a fully convolutional neural network to localize and perform refined segmentation of oral cavity-derived tumor regions in H&E-stained histological whole slide images. The proposed method uses color features in the HSV color model to identify tissue regions in a pre-processing… Mostrar más

The diagnosis of different types of cancer, including oral cavity-derived cancer, is made by a pathologist through complex and time-consuming microscopic analysis of tissue samples. This paper presents a method based on a fully convolutional neural network to localize and perform refined segmentation of oral cavity-derived tumor regions in H&E-stained histological whole slide images. The proposed method uses color features in the HSV color model to identify tissue regions in a pre-processing step to remove background and nonrelevant areas. The identified tissue regions are then transformed into the CIE L*a*b* color model and split into image-patches. The method was applied in a WSI dataset of oral squamous cell carcinoma tissue samples. In addition, for further validation and comparison with other proposals, we also applied the proposed method in a WSI dataset of sentinel lymph nodes with breast cancer metastases. Experimental evaluations were performed using a total of 85,621 image-patches of size 640 × 640 pixels and the proposed method achieved good results in different cancer-derived datasets with images of different tumors. The results revealed that the proposal is robust and capable to localize and perform refined segmentation, achieving accuracy results up to 97.6%, specificity up to 98.4%, and sensitivity up to 92.9%. The influence of different color spaces and different image-patch sizes in the proposed method also were explored. Mostrar menos

Oral epithelial dysplasia is a common type of pre-cancerous lesion that can be categorized as mild, moderate and severe. The manual diagnosis of this type of lesion is a time consuming and complex task. The use of digital systems applied to microscopic image analysis can aid the decision making of specialists. In recent years, deep learning-based methods are getting more attention due to its improved results in nuclei segmentation tasks. In this paper, we propose a methodology for nuclei… Mostrar más

Oral epithelial dysplasia is a common type of pre-cancerous lesion that can be categorized as mild, moderate and severe. The manual diagnosis of this type of lesion is a time consuming and complex task. The use of digital systems applied to microscopic image analysis can aid the decision making of specialists. In recent years, deep learning-based methods are getting more attention due to its improved results in nuclei segmentation tasks. In this paper, we propose a methodology for nuclei segmentation on images of dysplastic tissues using neural networks. Several optimization algorithms and color normalization methods were evaluated. The methodology was performed on a dataset of mice tongue images. The experimental evaluations showed that the Nadam optimizer in combination with images without the use of color normalization obtained the best results. The method was able to segment the images with an average accuracy of 0.887, the sensitivity of 0.762 and specificity of 0.942. The algorithm was compared to other segmentation methods and showed relevant results. These values indicate that the proposed method can be used as a tool to aid specialists in the nuclei analysis of histological images of the buccal cavity. Mostrar menos

Oral epithelial dysplasia is a common precancerous lesion type that can be graded as mild, moderate and severe. Although not all oral epithelial dysplasia become cancer over time, this premalignant condition has a significant rate of progressing to cancer and the early treatment has been shown to be considerably more successful. The diagnosis and distinctions between mild, moderate, and severe grades are made by pathologists through a complex and time-consuming process where some cytological… Mostrar más

Oral epithelial dysplasia is a common precancerous lesion type that can be graded as mild, moderate and severe. Although not all oral epithelial dysplasia become cancer over time, this premalignant condition has a significant rate of progressing to cancer and the early treatment has been shown to be considerably more successful. The diagnosis and distinctions between mild, moderate, and severe grades are made by pathologists through a complex and time-consuming process where some cytological features, including nuclear shape, are analysed. The use of computer-aided diagnosis can be applied as a tool to aid and enhance the pathologist decisions. Recently, deep learning based methods are earning more and more attention and have been successfully applied to nuclei segmentation problems in several scenarios. In this paper, we evaluated the impact of different color spaces transformations for automated nuclei segmentation on histological images of oral dysplastic tissues using fully convolutional neural networks (CNN). The CNN were trained using different color spaces from a dataset of tongue images from mice diagnosed with oral epithelial dysplasia. The CIE L*a*b* color space transformation achieved the best averaged accuracy over all analyzed color space configurations (88.2%). The results show that the chrominance information, or the color values, does not play the most significant role for nuclei segmentation purpose on a mice tongue histopathological images dataset. Mostrar menos

Dysplasia is a common oral premalignant lesion type that can be classified as mild, moderate and severe. The diagnosis of different types of dysplasia is made by a pathologist through complex and time consuming histological image analysis. The use of computer-aided diagnosis can be applied as a tool to aid and enhance the pathologists decisions. Recently, deep learning based methods are earning more and more attention and have been successfully applied to nuclei segmentation problems in several… Mostrar más

Dysplasia is a common oral premalignant lesion type that can be classified as mild, moderate and severe. The diagnosis of different types of dysplasia is made by a pathologist through complex and time consuming histological image analysis. The use of computer-aided diagnosis can be applied as a tool to aid and enhance the pathologists decisions. Recently, deep learning based methods are earning more and more attention and have been successfully applied to nuclei segmentation problems in several scenarios. In this paper, we present a method for automated nuclei segmentation on dysplastic oral tissues histological images using convolutional neural networks. We also evaluated the impact of color normalization techniques applied to the automated nuclei segmentation task on hematoxylin-eosin stained histological images. The proposed method achieves the best results overall when validated against other segmentation methods using a dataset composed of mice tongue histopathological images. Mostrar menos

Although, color is one of the most visually distinguishable visual properties, color alone is not enough to describe the content of images. The spatial organization of the different color regions also play an important role. In this paper, we propose and evaluate a new descriptor that combines information about color and about its spatial arrangement in an image. Moreover, the mechanism used to compute the descriptor provides for partial matching of images and for the development of… Mostrar más

Although, color is one of the most visually distinguishable visual properties, color alone is not enough to describe the content of images. The spatial organization of the different color regions also play an important role. In this paper, we propose and evaluate a new descriptor that combines information about color and about its spatial arrangement in an image. Moreover, the mechanism used to compute the descriptor provides for partial matching of images and for the development of efficient retrieval systems. We first describe the spatial arrangement of the color regions using a topological graph, where vertices represent the color regions and edges represent connections between regions and also the color differences between them. To compute the descriptor from this graph representation we use the spectral graph theory, avoiding the need for direct graph comparison. We performed various experimental evaluations to compare the accuracy of our new descriptor with descriptors based only on color, based only on topological information and a combination of both. Mostrar menos