Revisiting Lesion Tracking in 3D Total Body Photography

• URL: http://arxiv.org/abs/2412.07132v2
• Date: Tue, 24 Dec 2024 01:01:19 GMT
• Title: Revisiting Lesion Tracking in 3D Total Body Photography
• Authors: Wei-Lun Huang, Minghao Xue, Zhiyou Liu, Davood Tashayyod, Jun Kang, Amir Gandjbakhche, Misha Kazhdan, Mehran Armand,
• Abstract summary: Melanoma is the most deadly form of skin cancer. Despite prior work on longitudinal tracking of skin lesions in 3D total body photography, there are still several challenges. We propose a framework that takes in a pair of 3D textured meshes, matches lesions in the context of total body photography, and identifies unmatchable lesions.
• Score: 3.3844314021443025
• License:
• Abstract: Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and detecting new lesions across the body is essential for the early detection of melanoma. Despite prior work on longitudinal tracking of skin lesions in 3D total body photography, there are still several challenges, including 1) low accuracy for finding correct lesion pairs across scans, 2) sensitivity to noisy lesion detection, and 3) lack of large-scale datasets with numerous annotated lesion pairs. We propose a framework that takes in a pair of 3D textured meshes, matches lesions in the context of total body photography, and identifies unmatchable lesions. We start by computing correspondence maps bringing the source and target meshes to a template mesh. Using these maps to define source/target signals over the template domain, we construct a flow field aligning the mapped signals. The initial correspondence maps are then refined by advecting forward/backward along the vector field. Finally, lesion assignment is performed using the refined correspondence maps. We propose the first large-scale dataset for skin lesion tracking with 25K lesion pairs across 198 subjects. The proposed method achieves a success rate of 89.9% (at 10 mm criterion) for all pairs of annotated lesions and a matching accuracy of 98.2% for subjects with more than 200 lesions.

Related papers

• The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection [0.35348820263620895]
  iToBoS dataset includes 16,954 images of skin regions from 100 participants captured using 3D total body photography. Each image roughly corresponds to a $7 times 9$ cm section of skin with all suspicious lesions annotated using bounding boxes. This dataset aims to facilitate training and benchmarking of algorithms, with the goal of enabling early detection of skin cancer and deployment of this technology in non-clinical environments.
  arXiv  Detail & Related papers  (2025-01-30T11:10:44Z)
• RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis [56.57177181778517]
  RadGenome-Chest CT is a large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. We leverage the latest powerful universal segmentation and large language models to extend the original datasets.
  arXiv  Detail & Related papers  (2024-04-25T17:11:37Z)
• Weakly-Supervised Detection of Bone Lesions in CT [48.34559062736031]
  The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. We developed a pipeline to detect bone lesions in CT volumes via a proxy segmentation task. Our method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data.
  arXiv  Detail & Related papers  (2024-01-31T21:05:34Z)
• Skin Lesion Correspondence Localization in Total Body Photography [4.999387255024588]
  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.
  arXiv  Detail & Related papers  (2023-07-18T21:10:59Z)
• Skin Lesion Diagnosis Using Convolutional Neural Networks [0.30458514384586394]
  This project aims to address the issue by collecting state-of-the-art techniques for image classification from various fields. The models were trained using a dataset of 8012 images, and their performance was evaluated using 2003 images. It's worth noting that this model is trained end-to-end, directly from the image to the labels, without the need for handcrafted feature extraction.
  arXiv  Detail & Related papers  (2023-05-18T17:15:08Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• 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)
• Detector-SegMentor Network for Skin Lesion Localization and Segmentation [0.0]
  Melanoma is a life-threatening form of skin cancer when left undiagnosed at the early stages. We propose a network-in-network convolution neural network based approach for segmentation of the skin lesion.
  arXiv  Detail & Related papers  (2020-05-13T19:41:27Z)
• ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection [90.13718478362337]
  We introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals; and 3) Relation transfer module incorporates the semantic relationship. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.
  arXiv  Detail & Related papers  (2020-03-03T05:29:49Z)

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.