Manuscript Topics

Biomedical imaging has emerged as a major technological platform for cancer detection, diagnosis, and management due to being non-invasive and involving multi-dimensional data. An abundance of imaging data is collected daily, however, this wealth of information has not been utilized to its full extent. Therefore, there is a need for introducing novel biomedical image analysis techniques that are accurate, reproducible, and generalizable to large scale and multi-dimensional datasets.

Medical imaging modalities form an essential part of cancer clinical decision making and are able to furnish morphological, structural, metabolic and functional information. In particular, biomedical imaging has become an important element for early cancer detection, for determining the stage and the precise location of cancer lesions in computer-assisted surgery and other cancer treatments, and for checking if cancer has recurred. Longitudinal measurements can be obtained from serial scans, indicative of disease progression or regression, allowing noninvasive assessment of treatment effects.

This special issue solicits innovative biomedical image analysis techniques for cancer screening, diagnosis and staging, guiding cancer surgery, prognosticating cancer treatment outcomes, longitudinal monitoring of treatment outcomes, as well as diagnosing/monitoring for cancer recurrence. These methods may aim for segmentation, identification, and quantification of changes to tumor and normal tissues using radiomic and deep learning biomarkers as well as functional imaging markers, and classification of disease. These approaches may be applied to radiological imaging such as CT, MRI and ultrasound, or imaging at the cellular scale such as microscopy and digital pathology. Of particular interest are research contributions employing modern computer vision techniques, powered by statistical and machine/deep learning models, addressing the above challenges.

Key words: Machine/Deep Learning for Computer-aided Diagnosis and Prognosis, Biomedical Image Segmentation and Registration, Radiomics, Immunotherapies, Digital Pathology, Cell Segmentation and Tracking

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