Radiating Component Normalized Generalized Gradient Vector Flow Snake Untuk Segmentasi citra Sel Tunggal Smear Serviks

Pemeriksaan pap smear merupakan prosedur penapisan manual yang digunakan untuk mendeteksi sel-sel kanker serviks. Analisis hasil pemeriksaan pap smear secara manual memiliki banyak kelemahan yaitu membutuhkan banyak tenaga ahli dibidang patologi dan rawan terhadap kesalahan. Sebuah sistem penyaringan otomatis dan akurat untuk hasil pemeriksaan pap smear akan sangat bermanfaat dalam mengatasi kelemahan tersebut. Langkah yang paling utama dari sistem tersebut adalah proses segmentasi dari sel nukleus dan sitoplasma pada citra sel tunggal hasil pemeriksaan pap smear. Sebuah algoritme pengganti energi eksternal pada snake dapat digunakan untuk mendapatkan kontur nukleus dan sitoplasma pada citra sel tunggal smear serviks, salah satunya adalah Component Normalized Generalized Gradient Vector Flow Snake (CNGGVFS). Namun, CNGGVFS menggunakan fungsi edge map konvensional dalam memodelkan snake yang belum mampu mendeteksi daerah nukleus dari citra sel tunggal smear serviks dengan benar. Penelitian ini mengusulkan sebuah metode untuk segmentasi citra sel tunggal smear serviks menggunakan Radiating Component Normalized Generalized Gradient Vector Flow Snake (RCNGGVFS). Metode ini memanfaatkan perhitungan Radiating Edge Map (REM) dalam pencarian edge map pada metode CNGGVFS. Proses segmentasi pada penelitian ini terdiri atas 3 tahapan utama, yaitu: pra proses, segmentasi awal, dan segmentasi kontur. Pada tahap pra proses, citra sel tunggal smear serviks yang berada pada ruang warna RGB dikonversi ke dalam ruang warna CIELAB, dan kanal L dinormalisasi untuk mendapatkan citra grayscale. Kemudian, proses segmentasi awal pada penelitian ini menggunakan metode Fuzzy C-Means Non Local Spatial (FCM_NLS) untuk mendapatkan tekstur dari citra sel tunggal smear serviks. Tahap terakhir adalah tahap segmentasi kontur dengan metode RCNGGVFS sebagai fungsi energi eksternal snake yang bertujuan untuk mendapatkan kontur nukleus dan sitoplasma citra sel tunggal smear serviks yang lebih optimal. Berdasarkan uji coba, nilai rata-rata ZSI dan akurasi untuk segmentasi nukleus adalah 88,06% dan 95,34% . Kemudian, nilai rata-rata ZSI dan akurasi untuk segmentasi sitoplasma adalah 87,16% dan 83,48%. Hasil pengujian menunjukkan bahwa metode yang diusulkan mampu mendeteksi area nukleus lebih optimal dibanding dengan metode konvensional dan metode lainnya. ============================================================================================================ Pap smear test is a manual screening procedures that used to detect cervical cancer cells. Analysis of the results of Pap smear test by manual has many weaknesses, such as the need for experts in pathology in large numbers and the tendency for errors. An automatic and accurate screening system for pap smear test results will be very helpful in overcoming those weaknesses. The most important step of the screening system is segmentation of the nucleus and cytoplasm of the single cell image in Pap smear test results. An external force algorithm for active contour (snake) can be used to get the contour of the nucleus and cytoplasm of cervical smear image, for example Component Normalized Generalized Gradient Vector Flow Snake (CNGGVFS) method. However, CNGGVFS using a conventional calculation of edge map can not detect the nucleus area correctly in single cell cervical smear image segmentation. In this study, we proposed a new method for single cell cervical smear image segmentation using Radiating Component Normalized Generalized Gradient Vector Flow Snake (RCNGGVFS). This method used Radiating Edge Map (REM) calculation to search the edge map in CNGGVFS method. The segmentation process of this study consists of three main stages, they are: pre process, initial segmentation and contour segmentation. In the pre process stage, single cell cervical smear image will be converted from RGB color space into CIELAB color space, and the L channel is normalized to get a grayscale image. Then, in the initial segmentation process, Fuzzy C-Means Non Local Spatial (FCM_NLS) is used to get the texture of cervical smear images. The last stage is segmentation contour using RCNGGVFS as an external force for snake that aims to get the contour of the nucleus and cytoplasm of cervical smear image optimally. Based on the experimental result, the average value of ZSI and accuracy for nucleus segmentation is 88.06% and 95.34%. Then, the average value of ZSI and accuracy for cytoplasm segmentation is 87.16% and 83.48%. The experimental result show that the proposed method can detect the nucleus area optimally than the conventional method and other methods

Nuclei of cervical cells detection using deep learning

Orientador: Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Câncer de colo de útero é uma das principais causas de mortes por câncer entre as mulheres no mundo. Contudo, se o diagnóstico da doença for feito em estágios iniciais, as chances de cura aumentam significativamente. Estudos apontam que os núcleos de células cervicais podem sofrer alterações em caso de doença, além disso, dada sua estrutura e localização, sua detecção pode ser bastante útil para realizar outros tipos de análise nas células. Desta forma, ao longo dos anos, vários métodos que automaticamente detectam núcleos de células cervicais foram propostos para aprimorar a análise das imagens de teste de microscópio. Neste texto, iremos propor um método baseado em Redes Neurais Convolucionais para detectar automaticamente os núcleos de células cervicais. Após a Rede Neural Convolucional ser treinada com um conjunto de dados disponibilizados pelo Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014, suas camadas completamente conectadas são convertidas em camadas convolucionais para permitir o processamento de imagens de qualquer tamanho. Os resultados obtidos foram comparados com os obtidos pelos participantes que submeteram trabalhos com sucesso no ISBI 2014 e outros trabalhos que utilizaram o mesmo conjunto de dados. Nossos resultados experimentais indicaram que a metodologia proposta provê uma detecção de núcleos com métricas de precisão e recall comparáveis com os métodos do estado da arte em detecção de núcleos de células cervicais. Nos casos em que o tempo de processamento não seja um limitador, utilizando-se técnicas de morfologia matemática é possível melhorar ainda mais os resultados, obtendo-se valores para o recall que superam os melhores resultados descritos na literaturaAbstract: Cervical cancer is one of the most common causes of cancer death for women worldwide. However, if diagnosis occurs in an early stage of the disease, the chances of cure significantly increases. Studies have shown that changes on cervical cell¿s nucleus may occur in case of disease. Also, due to its structure and displacement, the detection of the nucleus can be very useful while performing other types of analysis in cervical cells. Through the years, various methods that automatically detect the nuclei of cervical cells have been proposed to improve the analysis of screening test images. In this work, we propose a Convolutional Neural Networks-based method that automatically detects the nuclei of cervical cells. Following training using a public dataset provided by the Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014, the network¿s fully connected layers are converted to convolutional layers to enable processing of images of any size. Our results were then compared with those achieved by other participants who successfully submitted their work to ISBI 2014 and other studies that used the same dataset. Our experimental results indicate that the methodology provides fast nuclei detection with precision and recall that are comparable with the state-of-the-art methods used to detect the nuclei of cervical cells. If the processing time is not an issue, it is possible to obtain even better results by applying morphological operations to previous results. In these case, it is possible to obtain recall results that surpass the best result described in the literatureMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Otimização de descritores usados nos estudos de cambios associadas à malignidade em imagens digitais de células cervicais

Orientadores: Marco Antonio Garcia de Carvalho, Guilherme Palermo CoelhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: O Câncer de Colo de Útero (CCU) é um problema de saúde coletiva em todo o mundo, nesse sentido foram feitos grandes avanços para sua detecção e prevenção. Apesar dos esforços feitos pelos países da América Latina para reduzir os indicadores de mortes por essa doença, eles ainda não são suficientes em comparação com o progresso de outros países europeus.Uma das razões, é que os sistemas de saúde pública em vários países da América têm limitações importantes em seus programas de acompanhamento e prevenção.O vírus do papanicolau está associado a 95 % dos cânceres cervicais, embora as instituições de saúde pública em todo o mundo invistam esforços técnicos, humanos e econômicos para reduzir o impacto da CCU em suas comunidades. Desde 1960, são realizadas pesquisas a respeito ao exame do Papanicolau, considerado este como um dos mecanismos mais utilizados pelo mundo para controlar e diagnosticar esta doença. Alterações Associadas à Malignidade (MAC), são pequenas alterações na morfologia e textura da cromatina, predizendo possíveis lesões malignas associadas ao CCU, tornando-se uma investigação interessante na aplicação do exame do panicolau. A identificação de MAC¿s em imagens de células cervicais é um problema accessível a possíveis investigações, devido às complexidades da identificação visual de estruturas nucleares. A partir das técnicas de Processamento Digital de Imagens (PDI), tem se conseguido grandes avanços, especialmente na obtenção de 400 descritores para o estudo de MAC's, no entanto a pequena quantidade de imagens focadas no estudo MAC, assim como a limitação técnica do equipamento e poucos profissionais que trabalham nesses estudos limitam o progresso nesta área. Esta tese tem como objetivo, otimizar descritores propostos na literatura para o estudo do MAC utilizando PDI. Para atingir este objetivo, foi criado em conjunto com a Fundação Universitária de Ciências para a Saúde da Colômbia (FUCS), um Data set de imagens de células cervicais que possibilitará o estudo de MAC's. Para adquirir imagens para o estudo, foram digitalizadas 6 folhas de pacientes com diferentes patologias que foram diagnosticadas e marcadas por uma cito-técnica especializada. As imagens foram pré-processadas empregando filtros espaciais e núcleos segmentados usando o algoritmo k-means e watershed. Os canais de cor foram separados pela sua contribuição de hematoxilina e corante Orange G6 dos núcleos segmentados; se extraíram 800 descritores morfológicos, de textura, densidade óptica e iluminação dos núcleos para sua posterior classificação. Contribuímos com a criação de um conjunto de dados para o estudo do MAC em imagens de CCU de exames de citologia convencional. Comparamos três classificadores supervisionados, treinados com 795 descritores, 412 descritores, 200 descritores e 962 instâncias. Calculamos e ordenamos os descritores extraídos pela informação obtida de cada um deles. Com um grupo de descritores, a precisão da classificação é 95,3 %. A segmentação dos núcleos mostrou uma precisão de 85,6 %. A otimização dos descritores foi de 4,3% melhor que a dos descritores propostos pela literatura, sendo composta por 30% de descritores de textura, 27% de descritores morfológicos, 11,5% de descritores de densidade óptica e 17% de descritores associados à concordância de níveis de cinzaAbstract: Cervical cancer (CCU) is a collective health problem worldwide, in that sense great advances have been made for its detection and prevention. Despite the efforts made by Latin American countries to reduce the indicators of deaths from this disease, they are still not sufficient compared to the progress of other European countries. One of the reasons is that the public health systems of several countries in the Americas present important limitations in their monitoring and prevention programs. The Human Papilloma Virus is associated with 95% of cervical cancers. Public health institutions around the world invest technical, human, and economic efforts to lessen the impact of the CCU on their communities. The mechanism most used by the world to control and diagnose this disease is the examination of the Human Papilloma. Research on this test has been conducted since 1960. The Malignancy Associated Changes MAC, are slight alterations in the morphology and texture of chromatin predicting possible malignant lesions associated to CCU, becoming one of the promising researches to be applied in the examination of the human papilloma. The identification of MAC's in cervical cell images is an open problem, due to the complexities of visual identification of nuclear structures. From Digital Image Processing (DIP) techniques great advances have been made especially in obtaining 400 descriptors for the study of MAC's, however the small amount of images focused on MAC's study, as well as the technical limitation of the equipment and few professionals who worked to these studies has limited progress in this area. The objective of this thesis is to optimize the descriptors proposed in the literature for the study of MAC using DIP. In order to achieve this objective, a set of cervical cell images was created for the study of MAC's, in conjunction with the Fundación Universitaria de Ciencias para la Salud-Colombia (FUCS). With the purpose of acquiring images for the study, 6 slides of patients with different pathologies were digitalized, which were diagnosed and labeled by a specialized cyto-technique. The images were pre-processed using spatial filters and segmented nuclei using the k-means and watershed algorithm. The color channels were separated by contribution of Hematoxylin and Orange G6 dye from the segmented nuclei; 800 morphological, texture, optical density and illumination descriptors were extracted from the nuclei for later classification. We contributed with the creation of a Data Set for the study of MAC in CCU images of conventional cytology examinations. We compared three supervised classifiers with 795 descriptors, 412 descriptors, 200 descriptors and 962 instances. We calculated and sorted the extracted descriptors by the information gain of each one of them. The optimization of the descriptors was 4.3% better than the descriptors proposed in the literature, consisting of 30% texture descriptors, 27% morphological descriptors, 11.5% optical density descriptors and 17% descriptors associated with the agreement of gray levelsDoutoradoSistemas de Informação e ComunicaçãoDoutor em TecnologiaCAPE

Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou

Preclinical MRI of the Kidney

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers

Preclinical MRI of the kidney : methods and protocols

This Open Access volume provides readers with an open access protocol collection and wide-ranging recommendations for preclinical renal MRI used in translational research. The chapters in this book are interdisciplinary in nature and bridge the gaps between physics, physiology, and medicine. They are designed to enhance training in renal MRI sciences and improve the reproducibility of renal imaging research. Chapters provide guidance for exploring, using and developing small animal renal MRI in your laboratory as a unique tool for advanced in vivo phenotyping, diagnostic imaging, and research into potential new therapies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Preclinical MRI of the Kidney: Methods and Protocols is a valuable resource and will be of importance to anyone interested in the preclinical aspect of renal and cardiorenal diseases in the fields of physiology, nephrology, radiology, and cardiology. This publication is based upon work from COST Action PARENCHIMA, supported by European Cooperation in Science and Technology (COST). COST (www.cost.eu) is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. PARENCHIMA (renalmri.org) is a community-driven Action in the COST program of the European Union, which unites more than 200 experts in renal MRI from 30 countries with the aim to improve the reproducibility and standardization of renal MRI biomarkers