Cell nuclei segmentation is one of the most important tasks in the analysis of biomedical images. With ever-growing sizes and amounts of three-dimensional images to be processed, there is a need for better and faster segmentation methods. Graph-based image segmentation has seen a rise in popularity in recent years, but is seen as very costly with regard to computational demand. We propose a new...

In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correc...

Pap smear test plays an important role for the early diagnosis of cervical cancer in which human cells taken from the cervix of patient are analysed for pre-cancerous changes. The manual analysis of these cells by expert cytologist is labor intensive and time consuming job. The automatic and accurate detection of cervical cells are two critical preprocessing steps for automatic Pap smear image ...

The Berkeley Drosophila Transcription Network Project (BDTNP) is developing a suite of methods that will allow a quantitative description and analysis of three dimensional (3D) gene expression patterns in an animal with cellular resolution. An important component of this approach are algorithms that segment 3D images of an organism into individual nuclei and cells and measure relative levels of...

Based on variational and level set approaches, we present a hybrid framework with quality control for confocal microscopy image segmentation. First, nuclei are modelled as blobs with additive noise and a filter derived from the Laplacian of a Gaussian kernel is applied for blob detection. Second, nuclei segmentation is reformulated as a front propagation problem and the energy minimization is o...

This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on narrow band level set implementation. Our method applies new multiscale analysis to estimate the number of clusters in each image region containing cells, which turns into input algorithm. We assess results three public cell databases. Overall, our outperformed six state-o...

Segmentation of cellular structures is of primary interest in cell imaging for a 3D reconstruction of cell shape. Such an analysis provides crucial information about cell morphology and is instrumental in understanding of biological processes leading to development of a particular pathology. The work presented in this paper reports on a novel method for segmentation of cellular structures (nucl...

Recent advance in fluorescence microscopy enables acquisition of 3D image volumes with better quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images. 3D segmentation using deep learning has achieved promising results in microscopy images. One issue is that deep learning techniques require a large set of groundt...

Automation-assisted reading (AAR) techniques have the potential to reduce errors and increase productivity in cervical cancer screening. The sensitivity of AAR relies heavily on automated segmentation of abnormal cervical cells, which is handled poorly by current segmentation algorithms. In this paper, a global and local scheme based on graph cut approach is proposed to segment cervical cells i...

Cluster division is a critical issue in fluorescence microscopy-based analytical cytology when preparation protocols do not provide appropriate separation of objects. Overlooking clustered nuclei and analyzing only isolated nuclei may dramatically increase analysis time or affect the statistical validation of the results. Automatic segmentation of clustered nuclei requires the implementation of...