Automated external cervical resorption segmentation in cone-beam CT using local texture features

• URL: http://arxiv.org/abs/2501.05236v1
• Date: Thu, 09 Jan 2025 13:43:01 GMT
• Title: Automated external cervical resorption segmentation in cone-beam CT using local texture features
• Authors: Sadhana Ravikumar, Asma A. Khan, Matthew C. Davis, Beatriz Paniagua,
• Abstract summary: External cervical resorption (ECR) is a resorptive process affecting teeth. cone-beam computed tomography (CBCT) is the recommended imaging modality for proper ECR assessment. Here, we present a method for ECR lesion segmentation that is based on automatic, binary classification of locally extracted voxel-wise texture features.
• Score: 0.14025454637917362
• License:
• Abstract: External cervical resorption (ECR) is a resorptive process affecting teeth. While in some patients, active resorption ceases and gets replaced by osseous tissue, in other cases, the resorption progresses and ultimately results in tooth loss. For proper ECR assessment, cone-beam computed tomography (CBCT) is the recommended imaging modality, enabling a 3-D characterization of these lesions. While it is possible to manually identify and measure ECR resorption in CBCT scans, this process can be time intensive and highly subject to human error. Therefore, there is an urgent need to develop an automated method to identify and quantify the severity of ECR resorption using CBCT. Here, we present a method for ECR lesion segmentation that is based on automatic, binary classification of locally extracted voxel-wise texture features. We evaluate our method on 6 longitudinal CBCT datasets and show that certain texture-features can be used to accurately detect subtle CBCT signal changes due to ECR. We also present preliminary analyses clustering texture features within a lesion to stratify the defects and identify patterns indicative of calcification. These methods are important steps in developing prognostic biomarkers to predict whether ECR will continue to progress or cease, ultimately informing treatment decisions.

Related papers

• Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images [0.06269281581001895]
  Intracranial hemorrhages (ICH) are routinely diagnosed using non-contrast CT (NCCT) for severity assessment. Deep learning (DL)-based methods have shown great potential, however, training them requires a huge amount of manually annotated lesion-level labels. We propose a novel weakly supervised DL method for ICH segmentation on NCCT scans, using image-level binary classification labels.
  arXiv  Detail & Related papers  (2023-09-28T17:32:19Z)
• Automated Identification of Failure Cases in Organ at Risk Segmentation Using Distance Metrics: A Study on CT Data [0.19661503834671132]
  Automated organ at risk (OAR) segmentation is crucial for radiation therapy planning in CT scans. The paper proposes a method to automatically identify failure cases by setting a threshold for the combination of Dice and Hausdorff distances.
  arXiv  Detail & Related papers  (2023-08-21T11:14:49Z)
• 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)
• Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models [4.780704816027884]
  We proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. Our top model achieved an average cross-validated ROC-AUC of 77.33 $pm$ 3.9%.
  arXiv  Detail & Related papers  (2023-02-08T15:41:21Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• 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)
• An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma [3.762671763237911]
  Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma. To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification.
  arXiv  Detail & Related papers  (2021-05-21T09:52:33Z)
• Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning [2.571523045125397]
  The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. The monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes.
  arXiv  Detail & Related papers  (2020-01-23T12:55:53Z)

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.