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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A survey on deep learning for skin lesion segmentation.

Zahra Mirikharaji¹, Kumar Abhishek¹, Alceu Bissoto²

¹Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada.

Medical Image Analysis

|June 21, 2023

Summary

This survey analyzes 177 deep learning studies for skin lesion segmentation. It examines data, models, and evaluation methods to improve computer-aided diagnosis for skin cancer.

Keywords:

Deep learning Segmentation Skin lesion Survey

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Last Updated: Jul 26, 2025

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Dermatology

Background:

Skin cancer poses a significant public health challenge.
Accurate skin lesion segmentation is crucial for computer-aided diagnosis.
Segmentation is complicated by artifacts, lesion characteristics, and imaging variations.

Purpose of the Study:

To systematically review deep learning-based skin lesion segmentation research.
To analyze current trends, methodologies, and limitations in the field.
To provide a comprehensive resource for researchers and practitioners.

Main Methods:

Conducted a systematic review of 177 research papers on deep learning for skin lesion segmentation.
Analyzed papers based on input data, model design, and evaluation metrics.
Examined seminal works and overall trends in the field.

Main Results:

Identified key dimensions: input data (datasets, preprocessing, synthetic data), model design (architecture, modules, losses), and evaluation (annotation, performance).
Discussed how methodological choices influence current trends and identified areas for improvement.
Summarized findings in comprehensive tables for comparison.

Conclusions:

Deep learning shows promise for skin lesion segmentation, aiding cancer diagnosis.
Further research is needed to address segmentation challenges and limitations.
The review provides a structured overview to guide future advancements in the field.