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1. Instance Segmentation of Neural Cells

   Yi, J.; Wu, P.; Jiang, M.; Hoeppner, D. J.; Metaxas, D. (September 2018, ECCV 2018 workshop)

   Instance segmentation of neural cells plays an important role in brain study. However, this task is challenging due to the special shapes and behaviors of neural cells. Existing methods are not precise enough to capture their tiny structures, e.g., filopodia and lamellipodia, which are critical to the understanding of cell interaction and behavior. To this end, we propose a novel deep multi-task learning model to jointly detect and segment neural cells instance-wise. Our method is built upon SSD, with ResNet101 as the backbone to achieve both high detection accuracy and fast speed. Furthermore, unlike existing works which tend to produce wavy and inaccurate boundaries, we embed a deconvolution module into SSD to better capture details. Experiments on a dataset of neural cell microscopic images show that our method is able to achieve better per- formance in terms of accuracy and efficiency, comparing favorably with current state-of-the-art methods. 

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2. PIXEL-WISE NEURAL CELL INSTANCE SEGMENTATION

   Yi, J.; Wu, P.; Hoeppner, D. J.; Metaxas, D. (April 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018))

   Accurate cell instance segmentation plays an important role in the study of neural cell interactions, which are critical for understanding the development of brain. These interactions are performed through the filopodia and lamellipodia of neural cells, which are extremely tiny structures and as a result render most existing instance segmentation methods powerless to precisely capture them. To solve this issue, in this paper we present a novel hierarchical neural network comprising object detection and segmentation modules. Compared to previous work, our model is able to efficiently share and make full use of the information at different levels between the two modules. Our method is simple yet powerful, and experimental results show that it captures the contours of neural cells, especially the filopodia and lamellipodia, with high accuracy, and outperforms recent state of the art by a large margin. 

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3. CONTEXT-REFINED NEURAL CELL INSTANCE SEGMENTATION

   Yi, J.; Wu, P.; Huang, Q.; Qu, H.; Hoeppner, D. J.; Metaxas, D. (April 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019))

   Neural cell instance segmentation serves as a valuable tool for the study of neural cell behaviors. In general, the instance segmentation methods compute the region of interest (ROI) through a detection module, where the segmentation is sub- sequently performed. To precisely segment the neural cells, especially their tiny and slender structures, existing work em- ploys a u-net structure to preserve the low-level details and encode the high-level semantics. However, such method is insufficient for differentiating the adjacent cells when large parts of them are included in the same cropped ROI. To solve this problem, we propose a context-refined neural cell instance segmentation model that learns to suppress the back- ground information. In particular, we employ a light-weight context refinement module to recalibrate the deep features and focus the model exclusively on the target cell within each cropped ROI. The proposed model is efficient and accurate, and experimental results demonstrate its superiority com- pared to the state-of-the-arts. 

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4. A hierarchical convolutional neural network for mitosis detection in phase-contrast microscopy images

   https://doi.org/10.1007/978-3-319-46723-8_79  

   Mao, Yunxiang; Yin, Zhaozheng (October 2016, MICCAI 2016: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016)

   We propose a Hierarchical Convolution Neural Network (HCNN) for mitosis event detection in time-lapse phase contrast microscopy. Our method contains two stages: first,we extract candidate spatial-temporal patch sequences in the input image sequences which potentially contain mitosis events. Then,we identify if each patch sequence contains mitosis event or not using a hieratical convolutional neural network. In the experiments,we validate the design of our proposed architecture and evaluate the mitosis event detection performance. Our method achieves 99.1% precision and 97.2% recall in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells and outperforms other state-of-the-art methods. Furthermore,the proposed method does not depend on hand-crafted feature design or cell tracking. It can be straightforwardly adapted to event detection of other different cell types. 

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5. Lineage‐Specific Mesenchymal Stromal Cells Derived from Human iPSCs Showed Distinct Patterns in Transcriptomic Profile and Extracellular Vesicle Production

   https://doi.org/10.1002/advs.202308975  

   Winston, Tackla; Song, Yuanhui; Shi, Huaiyu; Yang, Junhui; Alsudais, Munther; Kontaridis, Maria_I; Wu, Yaoying; Gaborski, Thomas_R; Meng, Qinghe; Cooney, Robert_N; et al (May 2024, Advanced Science)

   Abstract Over the past decades, mesenchymal stromal cells (MSCs) have been extensively investigated as a potential therapeutic cell source for the treatment of various disorders. Differentiation of MSCs from human induced pluripotent stem cells (iMSCs) has provided a scalable approach for the biomanufacturing of MSCs and related biological products. Although iMSCs shared typical MSC markers and functions as primary MSCs (pMSCs), there is a lack of lineage specificity in many iMSC differentiation protocols. Here, a stepwise hiPSC‐to‐iMSC differentiation method is employed via intermediate cell stages of neural crest and cytotrophoblast to generate lineage‐specific MSCs with varying differentiation efficiencies and gene expression. Through a comprehensive comparison between early developmental cell types (hiPSCs, neural crest, and cytotrophoblast), two lineage‐specific iMSCs, and six source‐specific pMSCs, are able to not only distinguish the transcriptomic differences between MSCs and early developmental cells, but also determine the transcriptomic similarities of iMSC subtypes to postnatal or perinatal pMSCs. Additionally, it is demonstrated that different iMSC subtypes and priming conditions affected EV production, exosomal protein expression, and cytokine cargo. 

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