Deadline Extension

Special Issue: Image Analysis in Advanced Skin Imaging Technology
Journal: Computer Methods and Programs in Biomedicine (2020 Impact Factor = 5.428)
Publisher: Elsevier

Introduction:
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Melanoma (also known as malignant melanoma) has one of the most rapidly increasing incidences in the world and has considerable mortality rate if left untreated. Early diagnosis is particularly important because melanoma can be cured with early excision. Advanced skin imaging technology is improving the way we detect melanoma. For example, the total-body 3-dimensional (3D) photography that constructs a digital 3D avatar of the patient can be used to view and monitor skin lesions across the total body, over time. Compared to current widespread manual dermoscopy and limited-access time-consuming 2D total body photography systems, total-body 3D photography brings new spatial and temporal capabilities, where skin lesions at different sites of the body and time can be detected simultaneously. This technology also opens opportunities for unmanned systems that will empower patients to perform their own skin check. This promising imaging technology are being used at Australian Centre of Excellence in Melanoma Imaging and Diagnosis (ACEMID), where 15 3D total-body photography (AUD$10 million) will be installed across Australia to revolutionize the early detection of melanoma. Many leading clinics including INOVA Melanoma and Skin Cancer Center, Dermatologic Surgery and Dermatology at Memorial Sloan Kettering Cancer, Skin Cancer Unit at the Hospital Clinic de Barcelona and Mount Sinai’s Waldman Melanoma Center have recently installed this promising technology.

The identification of melanoma from total-body 3D photography using human vision alone, however, also introduce new challenges. With the massive amounts of data collected from the total-body 3D photography, the challenges can be evidenced from tedious manual interpretation process and difficulties in re-identification of skin lesion across different time points. There are also the issues with subjectivity, inaccurate, and poorly reproducible results, even among experienced dermatologists. This is attributed to the challenges in interpreting skin lesion images where there can be diverse visual characteristics such as variations in size, shape boundaries (e.g., ‘fuzzy’), artifacts and has hairs. Therefore, image analysis is a valuable aid for clinical decision support (CDS) systems and for the image-based diagnosis of melanoma.

Despite these new imaging capabilities from the total-body 3D photography, the development of image analysis algorithms for skin lesion analysis has not kept pace, where the current focus of the algorithm development is still on single site and single time point. In addition, there is a need for methods that bridge the gap between advanced imaging technology and melanoma detection.

The goals of this special issue are to facilitate advancements and knowledge dissemination in image analysis for advanced skin imaging technology. Only high-quality and original research contributions will be considered. The special issue aims to cover, but not be limited to, the following topics:

# Image Analysis in 3D total-body Photography and 3D Skin Reconstructions
# Multi-site Melanoma Segmentation, Detection and Classification from Skin Images
# Image Analysis in Multi- and Cross-modality Skin Images
# Skin Lesion Tracking Over Sequential Images
# Registration of Cross-Modality and Sequential Skin Images
# Skin Lesion Feature Extraction and Content-Based Image Retrieval
# Skin Imaging Visualization (Spatial and Temporal)
# Software packages for 3D total-body photography
# Large-scale Public Skin Imaging Datasets

Publication Schedule:
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# Portal Opens: December 1st, 2021
# Manuscript Submission Deadline: July 1st, 2022
# Author Notification: September 1st, 2022
# Revised Papers Submission: October 1st, 2022
# Final Acceptance: December 1st, 2022

Guest Editorial Team:
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# Lei Bi, ARC Training Centre in Innovative BioEngineering, the University of Sydney, NSW, AU, lei.bi@sydney.edu.au
# Jinman Kim, School of Computer Science, the University of Sydney, NSW, AU, jinman.kim@sydney.edu.au
# Pablo Fernandez-Penas, Department of Dermatology, Westmead Hospital, NSW, AU, pablo.fernandezpenas@sydney.edu.au
# M. Emre Celebi, Department of Computer Science and Engineering, University of Central Arkansas, Conway, AR, USA, ecelebi@uca.edu
# Hitoshi Iyatomi, Faculty of Science and Engineering, Hosei University, Tokyo, Japan, iyatomi@hosei.ac.jp