Skin Lesion Correspondence Localization in Total Body Photography
- URL: http://arxiv.org/abs/2307.09642v2
- Date: Tue, 22 Aug 2023 17:19:34 GMT
- Title: Skin Lesion Correspondence Localization in Total Body Photography
- Authors: Wei-Lun Huang, Davood Tashayyod, Jun Kang, Amir Gandjbakhche, Michael
Kazhdan, Mehran Armand
- Abstract summary: We propose a novel framework combining geometric and texture information to localize skin lesion correspondence from a source scan to a target scan in total body photography (TBP)
As full-body 3D capture becomes more prevalent and has higher quality, we expect the proposed method to constitute a valuable step in the longitudinal tracking of skin lesions.
- Score: 4.999387255024588
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Longitudinal tracking of skin lesions - finding correspondence, changes in
morphology, and texture - is beneficial to the early detection of melanoma.
However, it has not been well investigated in the context of full-body imaging.
We propose a novel framework combining geometric and texture information to
localize skin lesion correspondence from a source scan to a target scan in
total body photography (TBP). Body landmarks or sparse correspondence are first
created on the source and target 3D textured meshes. Every vertex on each of
the meshes is then mapped to a feature vector characterizing the geodesic
distances to the landmarks on that mesh. Then, for each lesion of interest
(LOI) on the source, its corresponding location on the target is first coarsely
estimated using the geometric information encoded in the feature vectors and
then refined using the texture information. We evaluated the framework
quantitatively on both a public and a private dataset, for which our success
rates (at 10 mm criterion) are comparable to the only reported longitudinal
study. As full-body 3D capture becomes more prevalent and has higher quality,
we expect the proposed method to constitute a valuable step in the longitudinal
tracking of skin lesions.
Related papers
- μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation [2.012378666405002]
X-ray computed microtomography (mu-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples.
extracting relevant information from 3D images requires semantic segmentation of the regions of interest.
We propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus.
arXiv Detail & Related papers (2024-06-24T15:29:08Z) - Unsupervised correspondence with combined geometric learning and imaging
for radiotherapy applications [0.0]
The aim of this study was to develop a model to accurately identify corresponding points between organ segmentations of different patients for radiotherapy applications.
A model for simultaneous correspondence and estimation in 3D shapes was trained with head and neck organ segmentations from planning CT scans.
We then extended the original model to incorporate imaging information using two approaches.
arXiv Detail & Related papers (2023-09-25T16:29:18Z) - On the Localization of Ultrasound Image Slices within Point Distribution
Models [84.27083443424408]
Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US)
Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology.
We present a framework for automated US image slice localization within a 3D shape representation.
arXiv Detail & Related papers (2023-09-01T10:10:46Z) - Multi-View Vertebra Localization and Identification from CT Images [57.56509107412658]
We propose a multi-view vertebra localization and identification from CT images.
We convert the 3D problem into a 2D localization and identification task on different views.
Our method can learn the multi-view global information naturally.
arXiv Detail & Related papers (2023-07-24T14:43:07Z) - Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging [14.544274849288952]
We propose a 3D whole body imaging prototype to enable rapid evaluation and mapping of skin lesions.
A modular camera rig is designed to automatically capture synchronised images from multiple angles for entire body scanning.
We develop algorithms for 3D body image reconstruction, data processing and skin lesion detection based on deep convolutional neural networks.
arXiv Detail & Related papers (2022-05-14T15:24:06Z) - Generalizable Neural Performer: Learning Robust Radiance Fields for
Human Novel View Synthesis [52.720314035084215]
This work targets at using a general deep learning framework to synthesize free-viewpoint images of arbitrary human performers.
We present a simple yet powerful framework, named Generalizable Neural Performer (GNR), that learns a generalizable and robust neural body representation.
Experiments on GeneBody-1.0 and ZJU-Mocap show better robustness of our methods than recent state-of-the-art generalizable methods.
arXiv Detail & Related papers (2022-04-25T17:14:22Z) - Multiscale Analysis for Improving Texture Classification [62.226224120400026]
This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a texture separately.
We aggregate features extracted from gray and color texture images using bio-inspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix features, and Haralick statistical features into a single feature vector.
arXiv Detail & Related papers (2022-04-21T01:32:22Z) - Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D
Total-Body Skin Textured Meshes [13.93503694899408]
We present an automated approach to detect and longitudinally track skin lesions on 3D total-body skin surfaces scans.
The acquired 3D mesh of the subject is unwrapped to a 2D texture image, where a trained region convolutional neural network (R-CNN) localizes the lesions within the 2D domain.
Our results, on test subjects annotated by three human annotators, suggest that the trained R-CNN detects lesions at a similar performance level as the human annotators.
arXiv Detail & Related papers (2021-05-02T01:52:28Z) - Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection.
The proposed method constructs graph signals leveraging both local image features and global shape features.
Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
arXiv Detail & Related papers (2020-04-17T11:55:03Z) - Anatomy-aware 3D Human Pose Estimation with Bone-based Pose
Decomposition [92.99291528676021]
Instead of directly regressing the 3D joint locations, we decompose the task into bone direction prediction and bone length prediction.
Our motivation is the fact that the bone lengths of a human skeleton remain consistent across time.
Our full model outperforms the previous best results on Human3.6M and MPI-INF-3DHP datasets.
arXiv Detail & Related papers (2020-02-24T15:49:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.