Automatic tracing of mandibular canal pathways using deep learning
- URL: http://arxiv.org/abs/2111.15111v1
- Date: Tue, 30 Nov 2021 04:06:16 GMT
- Title: Automatic tracing of mandibular canal pathways using deep learning
- Authors: Mrinal Kanti Dhar and Zeyun Yu
- Abstract summary: Proper localization of the position of the mandibular canals, which surrounds the inferior alveolar nerve (IAN), reduces the risk of damaging it during dental implantology.
Here, we propose a deep learning-based framework to detect mandibular canals from CBCT data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: There is an increasing demand in medical industries to have automated systems
for detection and localization which are manually inefficient otherwise. In
dentistry, it bears great interest to trace the pathway of mandibular canals
accurately. Proper localization of the position of the mandibular canals, which
surrounds the inferior alveolar nerve (IAN), reduces the risk of damaging it
during dental implantology. Manual detection of canal paths is not an efficient
way in terms of time and labor. Here, we propose a deep learning-based
framework to detect mandibular canals from CBCT data. It is a 3-stage process
fully automatic end-to-end. Ground truths are generated in the preprocessing
stage. Instead of using commonly used fixed diameter tubular-shaped ground
truth, we generate centerlines of the mandibular canals and used them as ground
truths in the training process. A 3D U-Net architecture is used for model
training. An efficient post-processing stage is developed to rectify the
initial prediction. The precision, recall, F1-score, and IoU are measured to
analyze the voxel-level segmentation performance. However, to analyze the
distance-based measurements, mean curve distance (MCD) both from ground truth
to prediction and prediction to ground truth is calculated. Extensive
experiments are conducted to demonstrate the effectiveness of the model.
Related papers
- Extracting Training Data from Unconditional Diffusion Models [76.85077961718875]
diffusion probabilistic models (DPMs) are being employed as mainstream models for generative artificial intelligence (AI)
We aim to establish a theoretical understanding of memorization in DPMs with 1) a memorization metric for theoretical analysis, 2) an analysis of conditional memorization with informative and random labels, and 3) two better evaluation metrics for measuring memorization.
Based on the theoretical analysis, we propose a novel data extraction method called textbfSurrogate condItional Data Extraction (SIDE) that leverages a trained on generated data as a surrogate condition to extract training data directly from unconditional diffusion models.
arXiv Detail & Related papers (2024-06-18T16:20:12Z) - CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression [2.9998889086656586]
We introduce a novel methodology to reconstruct the kernel within the vascular network, which is a non-Euclidean space.
The proposed kernel encodes bothtemporal and vessel-to-vessel correlations, thus enabling blood flow reconstruction in vessels that lack direct measurements.
We demonstrate the performance of the model on three test cases, namely, a simple Y-shaped bifurcation, abdominal aorta, and the Circle of Willis in the brain.
arXiv Detail & Related papers (2024-03-14T15:41:15Z) - Dual-Stage Deeply Supervised Attention-based Convolutional Neural
Networks for Mandibular Canal Segmentation in CBCT Scans [4.140750794848906]
We propose a novel dual-stage deep learning based scheme for automatic detection of mandibular canal.
Particularly, we first we enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme.
After enhancement, we design 3D deeply supervised attention U-Net architecture for localize the volume of interest.
arXiv Detail & Related papers (2022-10-06T09:08:56Z) - Localized Perturbations For Weakly-Supervised Segmentation of Glioma
Brain Tumours [0.5801621787540266]
This work proposes the use of localized perturbations as a weakly-supervised solution to extract segmentation masks of brain tumours from a pretrained 3D classification model.
We also propose a novel optimal perturbation method that exploits 3D superpixels to find the most relevant area for a given classification using a U-net architecture.
arXiv Detail & Related papers (2021-11-29T21:01:20Z) - A Deep Learning Approach to Predicting Collateral Flow in Stroke
Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow.
The actual grading is mostly done through manual inspection of the acquired images.
We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
arXiv Detail & Related papers (2021-10-24T18:58:40Z) - Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and
Landmark Localization on 3D Intraoral Scans [56.55092443401416]
emphiMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.953pm0.076$, significantly outperforming the original MeshSegNet.
PointNet-Reg achieved a mean absolute error (MAE) of $0.623pm0.718, mm$ in distances between the prediction and ground truth for $44$ landmarks, which is superior compared with other networks for landmark detection.
arXiv Detail & Related papers (2021-09-24T13:00:26Z) - Simultaneous boundary shape estimation and velocity field de-noising in
Magnetic Resonance Velocimetry using Physics-informed Neural Networks [70.7321040534471]
Magnetic resonance velocimetry (MRV) is a non-invasive technique widely used in medicine and engineering to measure the velocity field of a fluid.
Previous studies have required the shape of the boundary (for example, a blood vessel) to be known a priori.
We present a physics-informed neural network that instead uses the noisy MRV data alone to infer the most likely boundary shape and de-noised velocity field.
arXiv Detail & Related papers (2021-07-16T12:56:09Z) - Imputation-Free Learning from Incomplete Observations [73.15386629370111]
We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation.
We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation.
Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
arXiv Detail & Related papers (2021-07-05T12:44:39Z) - Segmentation-free Estimation of Aortic Diameters from MRI Using Deep
Learning [2.231365407061881]
We propose a supervised deep learning method for the direct estimation of aortic diameters.
Our approach makes use of a 3D+2D convolutional neural network (CNN) that takes as input a 3D scan and outputs the aortic diameter at a given location.
Overall, the 3D+2D CNN achieved a mean absolute error between 2.2-2.4 mm depending on the considered aortic location.
arXiv Detail & Related papers (2020-09-09T18:28:00Z) - Online unsupervised deep unfolding for MIMO channel estimation [0.0]
We propose to perform online learning for channel estimation in a massive context.
This leads to a computationally efficient neural network that can be trained online when with an imperfect model.
It is applied to realistic channels and shows great performance, achieving channel estimation error almost as low as one would get with a perfectly calibrated system.
arXiv Detail & Related papers (2020-04-30T07:32:58Z)
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.