Special Issue "Biological Imaging in Tumor Microenvironment"
Deadline for manuscript submissions: 29 February 2024 | Viewed by 415
The tumor microenvironment comprises a heterogeneous population of tumor cells, immune cells, stromal cells, blood vessels, and extracellular matrix. The comprehensive profiling of compositions and interactions among distinct entities within the tumor microenvironment allows for a thorough understanding of tumor heterogeneity, progression, metastasis, therapeutic responses, resistances, etc. Biological imaging is among the most effective techniques for studying tumor microenvironments. Besides the structure and function, the context information of tissues and cells obtained from biological imaging enables the interrogation of the spatial relationships among bio-entities. Novel underexplored tissue and cellular patterns via biomedical image analysis have the potential to provide new insights. Artificial intelligence (AI) methods, such as deep learning, have revolutionized tissue/cell detection, segmentation, and recognition in biological imaging. Currently, AI-powered biological imaging analysis is an emerging topic in cancer research. This Special Issue aims to cover new advances in the analysis of biological imaging, including, but not limited to, microscopy imaging, fluorescence imaging, multiplex imaging, and nuclear imaging, for a better understanding of tumor microenvironments and their applications to tumor detection, diagnosis, prognosis, and treatment.
Dr. Pingjun Chen
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- tumor microenvironment
- tumor heterogeneity
- biological imaging
- biomedical image analysis
- whole slide imaging
- multiplex imaging
- nuclear imaging
- computational pathology
- spatial biology
- artificial intelligence
- deep learning
- predictive biomarkers