Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation

Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set

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

Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation

The utilisation of deep learning segmentation algorithms that learn complex organs and tissue patterns and extract essential regions of interest from the noisy background to improve the visual ability for medical image diagnosis has achieved impressive results in Medical Image Computing (MIC). This thesis focuses on retinal blood vessel segmentation tasks, providing an extensive literature review of deep learning-based medical image segmentation approaches while comparing the methodologies and empirical performances. The work also examines the limitations of current state-of-the-art methods by pointing out the two significant existing limitations: data size constraints and the dependency on high computational resources. To address such problems, this work proposes a novel efficient, simple multiview learning framework that contrastively learns invariant vessel feature representation by comparing with multiple augmented views by various transformations to overcome data shortage and improve generalisation ability. Moreover, the hybrid network architecture integrates the attention mechanism into a Convolutional Neural Network to further capture complex continuous curvilinear vessel structures. The result demonstrates the proposed method validated on the CHASE-DB1 dataset, attaining the highest F1 score of 83.46% and the highest Intersection over Union (IOU) score of 71.62% with UNet structure, surpassing existing benchmark UNet-based methods by 1.95% and 2.8%, respectively. The combination of the metrics indicates the model detects the vessel object accurately with a highly coincidental location with the ground truth. Moreover, the proposed approach could be trained within 30 minutes by consuming less than 3 GB GPU RAM, and such characteristics support the efficient implementation for real-world applications and deployments

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm