Mitosis Detection In Phase Contrast Microscopy Image Sequences Using Spatial Segmentation And Spatio-Temporal Localization Refinement

Verma, Ekansh; Singh, Vijendra; Safwan, Mohammed

doi:10.1109/isbi48211.2021.9433933

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…To enhance the temporal resolution of the detection, a 2D-U-Net [47] has been used to extract candidates in space that are then stacked and filtered in time with a Bidirectional Long Short-Term Memory (BLSTM) network to reject false positives [48]. To temporally resolve a mitosis in a spatiotemporal sequence, convolutional neural networks have been combined with a Two-Stream Bidirectional Long Short-Term Memory (TS-BLSTM) network [49][50][51].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy

et al. 2022

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A variety of biological and pharmaceutical studies, such as for anti-cancer drugs, require the quantification of cell responses over long periods of time. This is performed with time-lapse video microscopy that gives a long sequence of frames. For this purpose, phase contrast imaging is commonly used since it is minimally invasive. The cell responses of interest in this study are the mitotic cell divisions. Their manual measurements are tedious, subjective, and restrictive. This study introduces an automated method for these measurements. The method starts with preprocessing for restoration and reconstruction of the phase contrast time-lapse sequences. The data are first restored from intensity non-uniformities. Subsequently, the circular symmetry of the contour of the mitotic cells in phase contrast images is used by applying a Circle Hough Transform (CHT) to reconstruct the entire cells. The CHT is also enhanced with the ability to “vote” exclusively towards the center of curvature. The CHT image sequence is then registered for misplacements between successive frames. The sequence is subsequently processed to detect cell centroids in individual frames and use them as starting points to form spatiotemporal trajectories of cells along the positive as well as along the negative time directions, that is, anti-causally. The connectivities of different trajectories enhanced by the symmetry of the trajectories of the daughter cells provide as topological by-products the events of cell divisions together with the corresponding entries into mitoses as well as exits from cytokineses. The experiments use several experimental video sequences from three different cell lines with many cells undergoing mitoses and divisions. The quantitative validations of the results of the processing demonstrate the high performance and efficiency of the method.

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