AIMC Topic: Bone Marrow Cells

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Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing.

Communications biology
Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. H...

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence.

Acta cytologica
INTRODUCTION: The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a...

Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells.

Laboratory investigation; a journal of technical methods and pathology
Bone marrow aspirate (BMA) differential cell counts (DCCs) are critical for the classification of hematologic disorders. While manual counts are considered the gold standard, they are labor intensive, time consuming, and subject to bias. A reliable a...

Evaluating adipocyte differentiation of bone marrow-derived mesenchymal stem cells by a deep learning method for automatic lipid droplet counting.

Computers in biology and medicine
Stem cells are a group of competent cells capable of self-renewal and differentiating into osteogenic, chondrogenic, and adipogenic lineages. These cells provide the possibility of successfully treating patients. During differentiation into adipose t...

Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis.

Journal of medical systems
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization,...

Simultaneous Cell Detection and Classification in Bone Marrow Histology Images.

IEEE journal of biomedical and health informatics
Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many comput...

Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks.

IEEE journal of biomedical and health informatics
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation, we can obtain a rich mix of features that are highly reusable for var...

Human Wharton's Jelly-Derived Stem Cells Display a Distinct Immunomodulatory and Proregenerative Transcriptional Signature Compared to Bone Marrow-Derived Stem Cells.

Stem cells and development
Mesenchymal stromal cells (MSCs) are multipotent stem cells with immunosuppressive and trophic support functions. While MSCs from different sources frequently display a similar appearance in culture, they often show differences in their surface marke...

Efficient and robust cell detection: A structured regression approach.

Medical image analysis
Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and l...