Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis

• URL: http://arxiv.org/abs/2106.01700v2
• Date: Fri, 4 Jun 2021 07:56:43 GMT
• Title: Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis
• Authors: Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
• Abstract summary: Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder) Hand-crafted features, based on LocalBinary Patterns (LBP), were then extracted to describe the patellar texture. We used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the patellofemoral osteoarthritis (PFOA)
• Score: 3.609538870261841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. We used lateral view knee radiographs from MOST public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder). Hand-crafted features, based on LocalBinary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index(BMI), the total WOMAC score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve-average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting.Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC= 0.889, AP= 0.714). We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA.

Related papers

• A Federated Learning Framework for Stenosis Detection [70.27581181445329]
  This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA) Two heterogeneous datasets from two institutions were considered: dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy) dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature.
  arXiv  Detail & Related papers  (2023-10-30T11:13:40Z)
• How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers? [49.35105290167996]
  Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-08-17T20:40:30Z)
• Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments [1.1549572298362785]
  This study included subjects (1832 subjects, 3276 knees) from the baseline of the MOST study. PF joint regions-of-interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. Risk factors included age, sex, BMI and WOMAC score, and the radiographic osteoarthritis stage of the tibiofemoral joint (KL score)
  arXiv  Detail & Related papers  (2023-05-10T06:43:33Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification [0.8288807499147146]
  The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset.
  arXiv  Detail & Related papers  (2022-12-14T04:26:32Z)
• Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning [2.9822184411723645]
  Prior art focused on manually designed imaging biomarkers, which may not fully exploit all disease-related information present in MRI scan. In contrast, our method learns relevant representations from raw data end-to-end using Deep Learning. The method employs a 2D CNN to process the data slice-wise and aggregate the extracted features using a Transformer.
  arXiv  Detail & Related papers  (2022-01-26T10:17:41Z)
• 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)
• Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism [65.70943212672023]
  Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs. dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
  arXiv  Detail & Related papers  (2021-10-19T00:03:57Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST) [3.609538870261841]
  We present the first machine learning based automatic patellofemoral osteoarthritis (PFOA) detection method. Our deep learning based model trained on patella region from knee lateral view radiographs performs better at predicting PFOA than models based on patient characteristics and clinical assessments.
  arXiv  Detail & Related papers  (2021-01-12T08:37:55Z)
• CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images [0.0]
  We develop an automated model to identify stenosis severity in MPR images. The model predicts one of three classes: 'no stenosis' for normal, 'non-significant' - 1-50% of stenosis detected,'significant' - more than 50% of stenosis. For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level.
  arXiv  Detail & Related papers  (2020-01-23T15:20:22Z)

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.