DSpace Repository :: Browsing by Author "Ushizima, Daniela Mayumi"

Browsing by Author "Ushizima, Daniela Mayumi"

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Active contours for overlapping cervical cell segmentation.
(2021) Araujo, Flavio Henrique Duarte de; Silva, Romuere Rodrigues Veloso e; Medeiros, Fátima Nelsizeuma Sombra de; Rocha Neto, Jeová Farias Sales; Oliveira, Paulo Henrique Calaes; Bianchi, Andrea Gomes Campos; Ushizima, Daniela Mayumi
The nuclei and cytoplasm segmentation of cervical cells is a well studied problem. However, the current segmentation algorithms are not robust to clinical practice due to the high computational cost or because they cannot accurately segment cells with high overlapping. In this paper, we propose a method that is capable of segmenting both cytoplasm and nucleus of each individual cell in a clump of overlapping cells. The proposed method consists of three steps: 1) cellular mass segmentation; 2) nucleus segmentation; 3)cytoplasm identification based on an active contour method. We carried out experiments on both synthetic and real cell images. The performance evaluation of the proposed method showed that it was less sensitive to the increase in the number of cells per image and the overlapping ratio against two other existing algorithms. It has also achieved a promising low processing time and, hence, it has the potential to support expert systems for cervical cell recognition.
Ammonia gas sensor based on the frequency-dependent impedancecharacteristics of ultrathin polyaniline films.
(2016) Santos, Mirela de Castro; Ushizima, Daniela Mayumi; Bianchi, Andrea Gomes Campos; Pavinatto, Felippe J.; Bianchi, Rodrigo Fernando
This paper describes a variety of experiments that reveal the potential application of impedance spec-troscopy (IS) measurements to enhance performance of ultrathin polyaniline (PANI)-based films onflexible substrate to control ammonia gas exposure in the 0–20 ppm range. Further, the device is mechan-ically robust for making electrical contact without lithography. We have employed the IS technique toproper adjust the optimal operation frequency of an easy-to-process, inexpensive and user-friendly PANI-based resistometric detectors in which drift current and delay problems related to electrode effects anddielectric absorption of PANI are insignificant. These requirements place emphasis upon the material anddevice configuration, and the range of frequencies from 10 to 100 Hz corresponds to the ideal workingregion to operate the device to unify sensibility inside 5%, reproducibility, linearity, stability and lowresponse time (<1 min). These results are as efficient as those obtained using a more expensive commer-cial apparatus and can rival the affordability of traditional sensors. Linear Discriminant Analysis (LDA)and K-Means clustering confirmed that the device has the capacity to recognize ammonia concentrationaround 20 ppm with total accuracy of 95%. Our study provides evidence of a new way to improve theperformance of PANI-based sensors that can potentially be employed to monitor the environment oflivestock buildings, for example.
An ensemble method for nuclei detection of overlapping cervical cells.
(2021) Diniz, Débora Nasser; Vitor, Rafael Ferreira; Bianchi, Andrea Gomes Campos; Silva, Saul Emanuel Delabrida; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi; Medeiros, Fátima Nelsizeuma Sombra de; Souza, Marcone Jamilson Freitas
The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988).
A cytopathologist eye assistant for cell screening.
(2022) Diniz, Débora Nasser; Keller, Breno Nunes de Sena; Rezende, Mariana Trevisan; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Oliveira, Renata Rocha e Rezende; Luz, Eduardo José da Silva; Ushizima, Daniela Mayumi; Medeiros, Fátima Nelsizeuma Sombra de; Souza, Marcone Jamilson Freitas
Screening of Pap smear images continues to depend upon cytopathologists’ manual scrutiny, and the results are highly influenced by professional experience, leading to varying degrees of cell classification inaccuracies. In order to improve the quality of the Pap smear results, several efforts have been made to create software to automate and standardize the processing of medical images. In this work, we developed the CEA (Cytopathologist Eye Assistant), an easy-to-use tool to aid cytopathologists in performing their daily activities. In addition, the tool was tested by a group of cytopathologists, whose feedback indicates that CEA could be a valuable tool to be integrated into Pap smear image analysis routines. For the construction of the tool, we evaluate different YOLO configurations and classification approaches. The best combination of algorithms uses YOLOv5s as a detection algorithm and an ensemble of EfficientNets as a classification algorithm. This configuration achieved 0.726 precision, 0.906 recall, and 0.805 F1-score when considering individual cells. We also made an analysis to classify the image as a whole, in which case, the best configuration was the YOLOv5s to perform the detection and classification tasks, and it achieved 0.975 precision, 0.992 recall, 0.970 accuracy, and 0.983 F1-score.
Deep learning for cell image segmentation and ranking.
(2019) Araujo, Flavio Henrique Duarte de; Silva, Romuere Rodrigues Veloso e; Ushizima, Daniela Mayumi; Rezende, Mariana Trevisan; Carneiro, Claudia Martins; Bianchi, Andrea Gomes Campos; Medeiros, Fátima Nelsizeuma Sombra de
Ninety years after its invention, the Pap test continues to be the most used method for the early identification of cervical precancerous lesions. In this test, the cytopathologists look for microscopic abnormalities in and around the cells, which is a time-consuming and prone to human error task. This paper introduces computational tools for cytological analysis that incorporate cell segmentation deep learning techniques. These techniques are capable of processing both free-lying and clumps of abnormal cells with a high overlapping rate from digitized images of conventional Pap smears. Our methodology employs a preprocessing step that discards images with a low probability of containing abnormal cells without prior segmentation and, therefore, performs faster when compared with the existing methods. Also, it ranks outputs based on the likelihood of the images to contain abnormal cells. We evaluate our methodology on an image database of conventional Pap smears from real scenarios, with 108 fields-of-view containing at least one abnormal cell and 86 containing only normal cells, corresponding to millions of cells. Our results show that the proposed approach achieves accurate results (MAP = 0.936), runs faster than existing methods, and it is robust to the presence of white blood cells, and other contaminants.
Evaluation of three algorithms for the segmentation of overlapping cervical cells.
(2017) Lu, Zhi; Carneiro, Gustavo; Bradley, Andrew P.; Ushizima, Daniela Mayumi; Nosrati, Masoud S.; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Hamarneh, Ghassan
In this paper we introduce and evaluate the systems submitted to the first Overlapping Cervical Cytology Image Segmentation Challenge, held in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2014. This challenge was organized to encourage the development and benchmarking of techniques capable of segmenting individual cells from overlapping cellular clumps in cervical cytology images, which is a prerequisite for the development of the next generation of computer-aided diagnosis systems for cervical cancer. In particular, these automated systems must detect and accurately segment both the nucleus and cytoplasm of each cell, even when they are clumped together and hence partially occluded. However, this is an unsolved problem due to the poor contrast of cytoplasm boundaries; the large variation in size and shape of cells; the presence of debris and the large degree of cellular overlap. The challenge initially utilised a database of 16 high-resolution ( 40 magnification) images of complex cellular fields-of-view, in which the isolated real cells were used to construct a database of 945 cervical cytology images synthesised with a varying number of cells and degree of overlap, in order to provide full access of the segmentation ground truth. These synthetic images were used to provide a reliable and comprehensive framework for quantitative evaluation on this segmentation problem. Results from the submitted methods demonstrate all methods are effective in the segmentation of clumps containing at most three cells, with overlap coefficients up to 0.3. This highlights the intrinsic difficulty of this challenge and provides motivation for significant future improvement.
A hierarchical feature-based methodology to perform cervical cancer classification.
(2021) Diniz, Débora Nasser; Rezende, Mariana Trevisan; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi; Medeiros, Fátima Neusizeuma Sombra de; Souza, Marcone Jamilson Freitas
Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.
Hierarchical median narrow band for level set segmentation of cervical cell nuclei.
(2021) Braga, Alan Magalhães; Marques, Régis Cristiano Pinheiro; Medeiros, Fátima Nelsizeuma Sombra de; Rocha Neto, Jeová Farias Sales; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi
This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. These results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.
Hierarchical median narrow band for level set segmentation of cervical cell nuclei.
(2021) Braga, Alan Magalhães; Marques, Régis Cristiano Pinheiro; Medeiros, Fátima Neusizeuma Sombra de; Rocha Neto, Jeová Farias Sales; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi
This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. These results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.
Radial feature descriptors for cell classification and recommendation.
(2019) Silva, Romuere Rodrigues Veloso e; Araujo, Flavio Henrique Duarte de; Ushizima, Daniela Mayumi; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Medeiros, Fátima Nelsizeuma Sombra de
This paper introduces computational tools for cell classification into normal and abnormal, as well as content-based-image-retrieval (CBIR) for cell recommendation. It also proposes the radial feature descriptors (RFD), which define evenly interspaced segments around the nucleus, and proportional to the convexity of the nuclear boundary. Experiments consider Herlev and CRIC image databases as input to classification via Random Forest and bootstrap; we compare 14 different feature sets by means of False Negative Rate (FNR) and Kappa (k), obtaining FNR = 0.02 and k = 0.89 for Herlev, and FNR = 0.14 and k = 0.78 for CRIC. Next, we sort and rank cell images using convolutional neural networks and evaluate performance with the Mean Average Precision (MAP), achieving MAP = 0.84 and MAP = 0.82 for Herlev and CRIC, respectively. Cell classification show encouraging results regarding RFD, including its sensitivity to intensity variation around the nuclear membrane as it bypasses cytoplasm segmentation.
Reverse image search for scientific data within and beyond the visible spectrum.
(2018) Araujo, Flavio Henrique Duarte de; Silva, Romuere Rodrigues Veloso e; Medeiros, Fátima Nelsizeuma Sombra de; Parkinson, Dilworth Dula; Hexemer, Alexander; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi
The explosion in the rate, quality and diversity of image acquisition instruments has propelled the de- velopment of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper intro- duces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR , a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released.
Saliency-driven system models for cell analysis with deep learning.
(2019) Ferreira, Daniel Silva; Ramalho, Geraldo Luis Bezerra; Torres, Débora; Tobias, Alessandra Hermógenes Gomes; Rezende, Mariana Trevisan; Medeiros, Fátima Nelsizeuma Sombra de; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi
Background and objectives: Saliency refers to the visual perception quality that makes objects in a scene to stand out from others and attract attention. While computational saliency models can simulate the expert’s visual attention, there is little evidence about how these models perform when used to predict the cytopathologist’s eye fixations. Saliency models may be the key to instrumenting fast object detection on large Pap smear slides under real noisy conditions, artifacts, and cell occlusions. This paper describes how our computational schemes retrieve regions of interest (ROI) of clinical relevance using visual attention models. We also compare the performance of different computed saliency models as part of cell screening tasks, aiming to design a computer-aided diagnosis systems that supports cytopathologists. Method: We record eye fixation maps from cytopathologists at work, and compare with 13 different saliency prediction algorithms, including deep learning. We develop cell-specific convolutional neural networks (CNN) to investigate the impact of bottom-up and top-down factors on saliency prediction from real routine exams. By combining the eye tracking data from pathologists with computed saliency models, we assess algorithms reliability in identifying clinically relevant cells. Results: The proposed cell-specific CNN model outperforms all other saliency prediction methods, particularly regarding the number of false positives. Our algorithm also detects the most clinically relevant cells, which are among the three top salient regions, with accuracy above 98% for all diseases, except carcinoma (87%). Bottom-up methods performed satisfactorily, with saliency maps that enabled ROI detection above 75% for carcinoma and 86% for other pathologies. Conclusions: ROIs extraction using our saliency prediction methods enabled ranking the most relevant clinical areas within the image, a viable data reduction strategy to guide automatic analyses of Pap smear slides. Top-down factors for saliency prediction on cell images increases the accuracy of the estimated maps while bottom-up algorithms proved to be useful for predicting the cytopathologist’s eye fixations depending on parameters, such as the number of false positive and negative. Our contributions are: comparison among 13 state-of-the-art saliency models to cytopathologists’ visual attention and deliver a method that the associate the most conspicuous regions to clinically relevant cells.
Segmentação de núcleos de células cervicais em exame de Papanicolau.
(2018) Oliveira, Paulo Henrique Calaes; Bianchi, Andrea Gomes Campos; Bianchi, Andrea Gomes Campos; Moreira, Gladston Juliano Prates; Ushizima, Daniela Mayumi; Medeiros, Fátima Nelsizeuma Sombra de
A utilização de algoritmos que possam auxiliar no diagnostico do exame de Papanicolau vem sendo estudada ao longo das ultimas décadas devido ao aumento dos casos de Câncer cervical na população e respectivos dados coletados. Uma das etapas dessa automatização do diagnóstico é a segmentação automática das imagens. Alguns dos maiores problemas quando se realiza a segmentação deste tipo de imagens são a sobreposição celular, o dobramento das células e os artefatos que se confundem aos núcleos. Então é apresentada uma nova abordagem de segmentação nuclear utilizando uma heurística associada a um algoritmo genético multi-objetivo. O processo envolve três etapas principais, que são o pré-processamento, a calibração da heurística e a segmentação dos núcleos. Experimentos realizados com bases de dados sintéticas disponibilizadas no Overlapping Cervical Cytology Image Segmentation Challenge - ISBI2014 e uma nova base de imagens reais sugerem uma melhoria na detecção dos núcleos em comparação com os resultados obtidos pelos vencedores do desafio. Desse modo, esse trabalho apresenta uma interface web colaborativa criada para a geração de uma base de dados com imagens reais e um método para segmentação de núcleos que utiliza uma heurística associada a um algoritmo evolutivo multi-objetivo.

Browsing by Author "Ushizima, Daniela Mayumi"

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