VOTING-BASED SEGMENTATION OF OVERLAPPING NUCLEI IN CLARITY IMAGES

On the Variability in Cell and Nucleus Shapes

Cell morphology is an important regulator of cell function. Many abnormalities in cellular behavior can be discerned from changes in the shape of the cell and its organelles, typically the nucleus. Two major challenges for developing such phenotypic assays are reconstructing 3D surfaces of individual cells and nuclei from confocal images and developing characterizations of these surfaces for comparisons. We demonstrate two algorithms - 3D active contours and 3D condensed-attention UNet - to segment cells and nuclei from confocal images. The cell and nuclear surfaces are then converted into vectors using a reversible, spherical transform - i.e., shapes can be recovered from the vectors. Typical methods for characterizing shapes using size, shape, and image parameters such as area, volume, shape factor, solidity, and pixel intensities are not amenable to such reverse transformation. Our vector representation's principal component analysis shows that the significant modes of variability among cell and nucleus shapes are scaling and flattening. We benchmark these modes using a known mechanical model for nucleus morphology. Subsequent modes alter the eccentricity of the nucleus and translate and rotate it with respect to the cell. Our vector-space representation of cell and nucleus shape helps physically interpret the variability sources. It may further help to guide mechanical models and identify molecular mechanisms driving cell and nuclear shape changes.

Refinement of Convolutional Neural Network Based Cell Nuclei Detection Using Bayesian Inference

Cytological samples provide useful data for cancer diagnostics but their visual analysis under a microscope is tedious and time-consuming. Moreover, some scientific tests indicate that various pathologists can classify the same sample differently or the same pathologist can classify the sample differently if there is a long interval between subsequent examinations. We can help pathologists by providing tools for automatic analysis of cellular structures. Unfortunately, cytological samples usually consist of clumped structures, so it is difficult to extract single cells to measure their morphometric parameters. To deal with this problem, we are proposing a nuclei detection approach, which combines convolutional neural network and Bayesian inference. The input image is preprocessed by the stain separation procedure to extract a blue dye (hematoxylin) which is mainly absorbed by nuclei. Next, a convolutional neural network is trained to provide a semantic segmentation of the image. Finally, the segmentation results are post processed in order to detect nuclei. To do that, we model the nuclei distribution on a plane using marked point process and apply the Besag's iterated conditional modes to find the configuration of ellipses that fit the nuclei distribution. Thanks to this we can represent clusters of occluded cell nuclei as a set of an overlapping ellipses. The accuracy of the proposed method was tested on 50 cytological images of breast cancer. Reference data was generated by the manual labeling of cell nuclei in images. The effectiveness of the proposed method was compared with the marker-controlled watershed. We applied our method and marker controlled watershed to detect nuclei in the semantic segmentation maps generated by the convolutional neural network. The accuracy of nuclei detection is measured as the number of true positive (TP) detections and false positive (FP) detections. It was recorded that the method can detect correctly 93.5% of nuclei (TP) and at the same time it generates only 6.1% of FP. The proposed approach has led to better results than the marker-controlled watershed both in the number of correctly detected nuclei and in the number of false detections.