Image Analysis in Advanced Skin Imaging Technology Call for Papers

Call for Papers: Computer Methods and Programs in Biomedicine
special issue on 
Image Analysis in Advanced Skin Imaging Technology

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:
# 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:
# Lei Bi, ARC Training Centre in Innovative BioEngineering, the
University of Sydney, NSW, AU,

# Jinman Kim, School of Computer Science, the University of Sydney,

# Pablo Fernandez-Penas, Department of Dermatology, Westmead Hospital,

# M. Emre Celebi, Department of Computer Science and Engineering,
University of Central Arkansas, Conway, AR, USA,

# Hitoshi Iyatomi, Faculty of Science and Engineering, Hosei
University, Tokyo, Japan,