Artificial Intelligence, Segmentation, and Radiomics in Biomedical Imaging, Radiobiology and Biodiversity
A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2091
Special Issue Editors
2. Ri.MED Foundation, 90134 Palermo, Italy
3. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalu, PA, Italy
Interests: radiobiology; preclinical (in vitro and in vivo); PET/CT; MRI; CT; IVIS; gamma counter; molecular imaging; animal models and cell lines
Interests: ultrasound, CT, MRI and emergency with particular interest in urological disease; radiomics and artificial intelligence in clinical radiology
2. Laboratory of Computational Computer Vision (LCCV) in the School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, USA
Interests: biomedical image processing and analysis; artificial intelligence; machine learning; deep learning; computer vision
2. Research Affiliate Long Term, Laboratory of Computational Computer Vision (LCCV), School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Interests: biomedical image processing and analysis; radiomics; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: non-invasive imaging techniques: positron emission tomography (PET), computerized tomography (CT), and magnetic resonance (MR); radiomics and artificial intelligence in clinical health care applications; processing, quantification, and correction methods for ex vivo and in vivo medical images
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
New tracers/contrast agents, innovative radiopharmaceuticals, preclinical (in vitro and in vivo) radiobiology studies, advanced and new technologies, biodiversity and biotechnology, and innovative scanners are now available in molecular and conventional imaging. All these advancements can be further magnified by artificial intelligence (AI) approaches. In the last several decades, AI approaches have been used to support the segmentation and detection of lesions, the evaluation of radiopharmaceutical body biodistribution, the implementation of innovative radiomics prediction models, and the reduction of radiotracer/contrast agent dose and scanning time. Despite huge efforts worldwide, only a few such tools have been translated into clinical practice. In this Special Issue, we want to encourage colleagues involved in conventional and molecular imaging to deliver robust and reproducible AI preclinical and clinical applications, aiding in their adoption in the real world.
This Special Issue deals with, but is not limited to, the following topics:
- Machine- and deep-learning techniques for biomedical image analysis (i.e., segmentation of cells, tissues, organs, lesions; classification of cells, diseases, tumors, etc.);
- Image registration techniques;
- Image pre-processing techniques;
- Image-based 3D reconstruction;
- Radiomics and artificial intelligence for personalized medicine;
- Multimodality fusion (e.g., MRI, PET, CT, ultrasound) for diagnosis, image analysis and image-guided intervention;
- Machine and deep learning as tools to support medical diagnoses and decisions;
- Artificial intelligence in predicting treatment response and assessing disease prognosis;
- In vitro and in vivo preclinical assays and analysis tools for radiopharmaceutical validation;
- Radiopharmaceuticals biodistribution analysis of microPET/CT imaging on murine models;
- Theranostics;
- Biotechnology applied to biodiversity;
- Radiobiology analysis in vitro assays with tumor cell lines and in animal models of cancer.
Dr. Viviana Benfante
Dr. Giuseppe Salvaggio
Dr. Navdeep Dahiya
Dr. Albert Comelli
Dr. Alessandro Stefano
Guest Editors
Manuscript Submission Information
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Keywords
- biomedical image processing
- biomedical image classification
- biomedical image retrieval
- deep learning
- machine learning
- disease analysis
- diagnostic imaging
- radiomics
- artificial intelligence
- positron emission tomography
- ultrasound
- computed tomography
- X-ray
- magnetic resonance
- imaging
- non-invasive biomarkers
- molecular imaging
- small animal imaging techniques
- mouse atlas
- mouse imaging
- radiolabeled chelators
- radiopharmaceutical
- radiobiology
- theranostics
- polyphenols
- biodiversity
