Using machine learning on new feature sets extracted from 3D models of
broken animal bones to classify fragments according to break agent
- URL: http://arxiv.org/abs/2205.10430v1
- Date: Fri, 20 May 2022 20:16:21 GMT
- Title: Using machine learning on new feature sets extracted from 3D models of
broken animal bones to classify fragments according to break agent
- Authors: Katrina Yezzi-Woodley, Alexander Terwilliger, Jiafeng Li, Eric Chen,
Martha Tappen, Jeff Calder, Peter J. Olver
- Abstract summary: We present a new approach to fracture pattern analysis aimed at distinguishing bone fragments resulting from hominin bone breakage and those produced by carnivores.
This new method uses 3D models of fragmentary bone to extract a much richer dataset that is more transparent and replicable than feature sets previously used in fracture pattern analysis.
Supervised machine learning algorithms are properly used to classify bone fragments according to agent of breakage with average mean accuracy of 77% across tests.
- Score: 53.796331564067835
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Distinguishing agents of bone modification at paleoanthropological sites is
at the root of much of the research directed at understanding early hominin
exploitation of large animal resources and the effects those subsistence
behaviors had on early hominin evolution. However, current methods,
particularly in the area of fracture pattern analysis as a signal of marrow
exploitation, have failed to overcome equifinality. Furthermore, researchers
debate the replicability and validity of current and emerging methods for
analyzing bone modifications. Here we present a new approach to fracture
pattern analysis aimed at distinguishing bone fragments resulting from hominin
bone breakage and those produced by carnivores. This new method uses 3D models
of fragmentary bone to extract a much richer dataset that is more transparent
and replicable than feature sets previously used in fracture pattern analysis.
Supervised machine learning algorithms are properly used to classify bone
fragments according to agent of breakage with average mean accuracy of 77%
across tests.
Related papers
- BLAPose: Enhancing 3D Human Pose Estimation with Bone Length Adjustment [4.181969992118842]
This work introduces a recurrent neural network architecture designed to capture holistic information across entire video sequences.
We propose a novel augmentation strategy using synthetic bone lengths that adhere to physical constraints.
We fine-tune human pose estimation models using inferred bone lengths, observing notable improvements.
arXiv Detail & Related papers (2024-10-28T04:50:27Z) - ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic
Cartilage Segmentation [13.497950682194704]
We propose a novel Transformer-based segmentation model with improved biological priors that better distills morphologically diverse information through conditional mechanisms.
Experiments on the mice cartilage dataset show the superiority of our new model compared to other competitive segmentation models.
arXiv Detail & Related papers (2024-02-06T04:30:49Z) - Shape Matters: Detecting Vertebral Fractures Using Differentiable
Point-Based Shape Decoding [51.38395069380457]
Degenerative spinal pathologies are highly prevalent among the elderly population.
Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability.
In this study, we specifically explore the use of shape auto-encoders for vertebrae.
arXiv Detail & Related papers (2023-12-08T18:11:22Z) - Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral
Fracture Grading [72.45699658852304]
This paper proposes a novel approach to train a generative Diffusion Autoencoder model as an unsupervised feature extractor.
We model fracture grading as a continuous regression, which is more reflective of the smooth progression of fractures.
Importantly, the generative nature of our method allows us to visualize different grades of a given vertebra, providing interpretability and insight into the features that contribute to automated grading.
arXiv Detail & Related papers (2023-03-21T17:16:01Z) - Breast Cancer Induced Bone Osteolysis Prediction Using Temporal
Variational Auto-Encoders [65.95959936242993]
We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions.
It will assist in planning and evaluating treatment strategies to prevent skeletal related events (SREs) in breast cancer patients.
arXiv Detail & Related papers (2022-03-20T21:00:10Z) - 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) - Adversarial Regression Learning for Bone Age Estimation [6.942003070153651]
We propose an adversarial regression learning network (ARLNet) for bone age estimation.
Specifically, we first extract bone features from a fine-tuned Inception V3 neural network.
We then propose adversarial regression loss and feature reconstruction loss to guarantee the transition from training data to test data.
arXiv Detail & Related papers (2021-03-10T15:58:26Z) - Generative Modelling of 3D in-silico Spongiosa with Controllable
Micro-Structural Parameters [1.0804061924593265]
We propose to apply recent advances in generative adversarial networks to generate realistic bone structures in-silico.
In a first step, we trained a volumetric generative model in a progressive manner using a Wasserstein objective and gradient penalty.
We were able to simulate the resulting bone structure after deterioration or treatment effects of osteoporosis therapies.
arXiv Detail & Related papers (2020-09-23T18:11:47Z) - 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.