Barcelo, S. | Search bioRxiv

bioRxiv2023

Ensemble Processing and Synthetic Image Generation For Abnormally Shaped Nuclei Segmentation

Abnormalities in biological cell nuclei morphology are correlated with cell cycle stages, disease states, and various external stimuli. There have been many deep learning approaches that have described nuclei segmentation and analysis of nuclear morphology. One problem with many deep learning methods is acquiring large amounts of annotated nuclei data, which is generally expensive to obtain. In this paper, we propose a system to segment abnormally shaped nuclei with a limited amount of training data. We first generate specific shapes of synthetic nuclei groundtruth. We randomly sample these synthetic groundtruth images into training sets to train several Mask R-CNNs. We design an ensemble strategy to combine or fuse segmentation results from the Mask R-CNNs. We also design an oval nuclei removal by StarDist to reduce the false positives and improve the overall segmentation performance. Our experiments indicate that our method outperforms other methods in segmenting abnormally shaped nuclei.

cell biology↗

bioRxiv2023

Label-Free Mammalian Cell Tracking Enhanced by Precomputed Velocity Fields

Label-free cell imaging, where the cell is not "labeled" or modified by fluorescent chemicals, is an important research area in the field of biology. It avoids altering the cells properties which typically happens in the process of chemical labeling. However, without the contrast enhancement from the label, the analysis of label-free imaging is more challenging than label-based imaging. In addition, it provides few human interpretable features, and thus needs machine learning approaches to help with the identification and tracking of specific cells. We are interested in label-free phase contrast imaging to track cells flowing in a cell sorting device where images are acquired at 500 frames/s. Existing Multiple Object Tracking (MOT) methods face four major challenges when used for tracking cells in a microfluidic sorting device: (i) most of the cells have large displacements between frames without any overlap; (ii) it is difficult to distinguish between cells as they are visually similar to each other; (iii) the velocities of cells vary with the location in the device; (iv) the appearance of cells may change as they move in and out of the focal plane of the imaging sensor that observes the isolation process. In this paper, we introduce a method for tracking cells in a predefined flow in the sorting device via phase contrast microscopy. Our proposed method is based on DeepSORT and YOLOv4 and exploits prior knowledge of a cells velocity to assist tracking. We modify the Kalman filter in DeepSORT to accommodate a non-constant velocity motion model and integrate a representative velocity field obtained from fluid dynamics into the Kalman filter. The experimental results show that our proposed method outperforms several MOT methods for tracking cells in the sorting device.

cell biology↗