Landmark detection in Cardiac Magnetic Resonance Imaging Using A Convolutional Neural Network

• URL: http://arxiv.org/abs/2008.06142v1
• Date: Fri, 14 Aug 2020 00:25:59 GMT
• Title: Landmark detection in Cardiac Magnetic Resonance Imaging Using A Convolutional Neural Network
• Authors: Hui Xue, Jessica Artico, Marianna Fontana, James C Moon, Rhodri H Davies, Peter Kellman
• Abstract summary: This retrospective study included cine, LGE and T1 mapping scans from two hospitals. CNN models were developed to detect two mitral valve plane and apical points on long-axis (LAX) images. No significant differences were found for the anterior RV insertion angle and LV length by the models and operators for all views and imaging sequences.
• Score: 4.6250189957415255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: To develop a convolutional neural network (CNN) solution for robust landmark detection in cardiac MR images. Methods: This retrospective study included cine, LGE and T1 mapping scans from two hospitals. The training set included 2,329 patients and 34,019 images. A hold-out test set included 531 patients and 7,723 images. CNN models were developed to detect two mitral valve plane and apical points on long-axis (LAX) images. On short-axis (SAX) images, anterior and posterior RV insertion points and LV center were detected. Model outputs were compared to manual labels by two operators for accuracy with a t-test for statistical significance. The trained model was deployed to MR scanners. Results: For the LAX images, success detection was 99.8% for cine, 99.4% for LGE. For the SAX, success rate was 96.6%, 97.6% and 98.9% for cine, LGE and T1-mapping. The L2 distances between model and manual labels were 2 to 3.5 mm, indicating close agreement between model landmarks to manual labels. No significant differences were found for the anterior RV insertion angle and LV length by the models and operators for all views and imaging sequences. Model inference on MR scanner took 610ms/5.6s on GPU/CPU, respectively, for a typical cardiac cine series. Conclusions: This study developed, validated and deployed a CNN solution for robust landmark detection in both long and short-axis CMR images for cine, LGE and T1 mapping sequences, with the accuracy comparable to the inter-operator variation.

Related papers

• TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images [62.53931644063323]
  In this study we extended the capabilities of TotalSegmentator to MR images. We trained an nnU-Net segmentation algorithm on this dataset and calculated similarity coefficients (Dice) to evaluate the model's performance. The model significantly outperformed two other publicly available segmentation models (Dice score 0.824 versus 0.762; p0.001 and 0.762 versus 0.542; p)
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• 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)
• 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)
• Deep learning-based detection of intravenous contrast in computed tomography scans [0.7313653675718069]
  Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. We developed and validated a CNN-based deep learning platform to identify IV contrast within CT scans.
  arXiv  Detail & Related papers  (2021-10-16T00:46:45Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images [2.0791118244420757]
  A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence and plane. High sequence and plane accuracies were observed for single vendor training (SVT) and multi-vendor training (MVT) There was high accuracy for common sequences and conventional cardiac planes.
  arXiv  Detail & Related papers  (2021-09-17T11:42:39Z)
• Dopamine Transporter SPECT Image Classification for Neurodegenerative Parkinsonism via Diffusion Maps and Machine Learning Classifiers [0.0]
  This study aims to provide an automatic and robust method to classify the SPECT images into two types, namely Normal and Abnormal DaT-SPECT image groups. The 3D images of N patients are mapped to an N by N pairwise distance matrix and training set are embedded into a low-dimensional space by using diffusion maps. The feasibility of the method is demonstrated via Parkinsonism Progression Markers Initiative (PPMI) dataset of 1097 subjects and a clinical cohort from Kaohsiung Chang Gung Memorial Hospital (KCGMH-TW) of 630 patients.
  arXiv  Detail & Related papers  (2021-04-06T06:30:15Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography [5.973882600944421]
  A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-positive reduction. Our model showed statistically higher accuracy, sensitivity, and specificity when compared to the available model at 0.25 FPPV and the best F-1 score.
  arXiv  Detail & Related papers  (2020-05-22T10:49:23Z)
• 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.