Computational modeling in medical image analysis

Dear Colleagues,

Quantitative radiology (QR), when brought to routine clinical practice, will bring about a significant enhancement of the role of radiology in the medical milieu, potentially spawning numerous new advances in medicine. Currently, more and more medical images (MRI, CT, Ultrasound, PETCT, OCT, etc.) are being collected and analyzed for disease quantification body-region-wide or bodywide in patients with cancer and/or disease conditions, and clinical tasks related with medical images including screening, detection/diagnosis, staging, prognosis assessment, treatment planning, treatment prediction assessment, treatment response assessment, and restaging/surveillance. New algorithms for medical image processing will play the role of an engineer for the above clinical tasks.

In this Special Issue, we shall focus on the vast range of new algorithms for medical image processing, analysis, and quantification. Machine learning, especially deep learning, has recently been widely investigated and has shown its power in medical image segmentation, registration, classification, responde prediction, etc. We welcome manuscripts using unsupervised or supervised learning based on statistical and mathematical models for all the above clinical tasks in this Special Issue. Other topics include but are not limited to new algorithms on medical image segmentation, registration, disease response prediction, classification, image quality enhancement, image construction, and new systems in computer-aided diagnosis, perception, image-guided procedures, biomedical applications, informatics, radiology, and digital pathology.

Prof. Dr. Syoji Kobashi
Guest Editor

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