# Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging

arXiv: https://arxiv.org/abs/2205.07085 · HTML: https://mlsanity.com/paper/2205.07085/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 89 references that are in the map.
- First posted on arXiv 14 May 2022.

## Builds on (most cited first, 22 of 22)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021)
- [3D-CODED : 3D Correspondences by Deep Deformation](https://mlsanity.com/paper/1806.05228/) (2018)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones](https://mlsanity.com/paper/2007.00478/) (2020)
- [LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration](https://mlsanity.com/paper/2010.12447/) (2020)
- [Learning elementary structures for 3D shape generation and matching](https://mlsanity.com/paper/1908.04725/) (2019)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [A Survey of Algorithms for Geodesic Paths and Distances](https://mlsanity.com/paper/2007.10430/) (2020)
- [Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features](https://mlsanity.com/paper/1810.09487/) (2018)
- [Can self-training identify suspicious ugly duckling lesions?](https://mlsanity.com/paper/2105.07116/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis](https://mlsanity.com/paper/2311.15836/) (2023)
- [Revisiting Lesion Tracking in 3D Total Body Photography](https://mlsanity.com/paper/2412.07132/) (2024)
- [A Shape-Aware Total Body Photography System for In-focus Surface Coverage Optimization](https://mlsanity.com/paper/2505.16228/) (2025)

## Abstract (arXiv)

Advanced artificial intelligence and machine learning have great potential to redefine how skin lesions are detected, mapped, tracked and documented. Here, We propose a 3D whole-body imaging system known as 3DSkin-mapper to enable automated detection, evaluation and mapping of skin lesions. A modular camera rig arranged in a cylindrical configuration was designed to automatically capture images of the entire skin surface of a subject synchronously from multiple angles. Based on the images, we developed algorithms for 3D model reconstruction, data processing and skin lesion detection and tracking based on deep convolutional neural networks. We also introduced a customised, user-friendly, and adaptable interface that enables individuals to interactively visualise, manipulate, and annotate the images. The proposed system is developed for skin lesion screening, the focus of this paper is to introduce the system instead of clinical study. Using synthetic and real images we demonstrate the effectiveness of the proposed system by providing multiple views of a target skin lesion, enabling further 3D geometry analysis and longitudinal tracking. It takes only a few seconds to capture the entire skin surface, and about half an hour to process and analyse the images. Our experiments show that the proposed system allow fast and easy whole body 3D imaging. It can be used by dermatological clinics to conduct skin screening, detect and track skin lesions over time, identify suspicious lesions, and document pigmented lesions. The system can potentially save clinicians time and effort significantly. The 3D imaging and analysis has the potential to change the paradigm of whole body photography with many applications in skin diseases, including inflammatory and pigmentary disorders.