Deep Learning Approaches for Prenatal and Perinatal-Image Analysis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: closed (10 October 2021) | Viewed by 510
Special Issue Editors
Interests: artificial intelligence; computer vision; robotics; remote sensing
Interests: deep learning; machine learning; medical-image analysis
Special Issues, Collections and Topics in MDPI journals
Interests: machine learing; deep learning; biomedical imaging
Special Issue Information
Dear Colleagues,
Sensors welcomes submissions to this Special Issue on “Deep Learning Approaches for Prenatal and Perinatal-Image Analysis”.
In recent years, the use of deep learning techniques in neonatology has increased. The development of these automated methods has produced promising results and paved the way for improved workflow efficiency, diagnostics, segmentation, survival probability forecasting, among others. Furthermore, thanks to the release of publicity available datasets, this field is also becoming increasingly accessible to researchers. However, several challenges still need to be addressed. This Special Issue encourages authors, from academia and industry, to submit new research results about technological innovations and novel applications for image analysis for prenatal and perinatal applications, with special interest to deep learning algorithms. The Special Issue topics include, but are not limited to:
- Obstetric ultrasound for diagnosis and biometric measurements
- RGB-D sensors for infants monitoring
- Intra-operative imaging for obstetric surgery (e.g., laparoscopy)
- Structure segmentation and classification
- Generative models
- Biometrics
- Challenges and datasets
Dr. Emanuele Frontoni
Dr. Sara Moccia
Dr. Aldo Marzullo
Guest Editors
Manuscript Submission Information
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