Segmentasi Citra Sel Tunggal Smear Serviks Menggunakan Metode Radiating Normally Biased Generalized Gradient Vector Flow Snake

Sebuah sistem penyaringan otomatis dan sistem diagnosa yang akurat sangat berguna untuk proses analisis hasil pemeriksaan pap smear. Langkah yang paling utama dari sistem tersebut adalah proses segmentasi sel nukleus dan sitoplasma pada citra hasil pemeriksaan pap smear, karena dapat memengaruhi keakuratan sistem. Normally Biased Generalized Gradient Vector Flow Snake (NBGGVFS) merupakan sebuah algoritma gaya eksternal untuk active contour (snake) yang menggabungkan metode Generalized Gradient Vector Flow Snake (GGVFS) dan Normally Biased Gradient Vector Flow Snake (NBGVFS). Dalam memodelkan snake, terdapat fungsi edge map. Edge map biasanya dihitung dengan menggunakan operator deteksi tepi seperti sobel. Namun, metode ini tidak dapat mendeteksi daerah nukleus dari citra smear serviks dengan benar. Penelitian ini bertujuan untuk segmentasi citra sel tunggal smear serviks dengan memanfaatkan penggunaan Radiating Edge Map untuk menghitung edge map dari citra dengan metode NBGGVFS. Metode yang diusulkan terdiri atas tiga tahapan utama, yaitu tahap praproses, segmentasi awal dan segmentasi kontur. Uji coba dilakukan dengan menggunakan data set Herlev. Pengujian dilakukan dengan membandingkan hasil segmentasi metode yang diusulkan dengan metode pada penelitian sebelumnya dalam melakukan segmentasi citra sel tunggal smear serviks. Hasil pengujian menunjukkan bahwa metode yang diusulkan mampu mendeteksi area nukleus lebih optimal metode penelitian sebelumnya. Nilai rata-rata akurasi dan Zijdenbos Similarity Index (ZSI) untuk segmentasi nukleus adalah 96,96% dan 90,68%. Kemudian, nilai rata-rata akurasi dan ZSI untuk segmentasi sitoplasma adalah 86,78% and 89,35%. Dari hasil evaluasi tersebut, disimpulkan metode yang diusulkan dapat digunakan sebagai proses segmentasi citra smear serviks pada identifikasi kanker serviks secara otomatis

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