Automatic CAD-RADS Scoring Using Deep Learning

• URL: http://arxiv.org/abs/2010.01963v1
• Date: Mon, 5 Oct 2020 12:48:15 GMT
• Title: Automatic CAD-RADS Scoring Using Deep Learning
• Authors: Felix Denzinger, Michael Wels, Katharina Breininger, Mehmet A. G\"uls\"un, Max Sch\"obinger, Florian Andr\'e, Sebastian Bu\ss, Johannes G\"orich, Michael S\"uhling, Andreas Maier
• Abstract summary: The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries.
• Score: 5.789689435835229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coronary CT angiography (CCTA) has established its role as a non-invasive modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting and Data System (CAD-RADS) has been developed to standardize communication and aid in decision making based on CCTA findings. The CAD-RADS score is determined by manual assessment of all coronary vessels and the grading of lesions within the coronary artery tree. We propose a bottom-up approach for fully-automated prediction of this score using deep-learning operating on a segment-wise representation of the coronary arteries. The method relies solely on a prior fully-automated centerline extraction and segment labeling and predicts the segment-wise stenosis degree and the overall calcification grade as auxiliary tasks in a multi-task learning setup. We evaluate our approach on a data collection consisting of 2,867 patients. On the task of identifying patients with a CAD-RADS score indicating the need for further invasive investigation our approach reaches an area under curve (AUC) of 0.923 and an AUC of 0.914 for determining whether the patient suffers from CAD. This level of performance enables our approach to be used in a fully-automated screening setup or to assist diagnostic CCTA reading, especially due to its neural architecture design -- which allows comprehensive predictions.

Related papers

• Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning [3.656984996633334]
  This study introduces an end-to-end machine learning solution developed as part of our solution for the MICCAI 2023 Automatic Region-based Coronary Artery Disease diagnostics. It aims to benchmark solutions for multivessel coronary artery segmentation and potential stenotic lesion localisation from X-ray coronary angiograms. Our solution achieved a mean F1 score of $37.69%$ for coronary artery segmentation, and $39.41%$ for stenosis localisation, positioning our team in the 5th position on both leaderboards.
  arXiv  Detail & Related papers  (2023-10-27T08:03:12Z)
• Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients [26.50934421749854]
  We propose a multi-task deep learning model to support risk stratification and down-stream test selection. Our model achieved an Area Under the receiver operating characteristic Curve (AUC) of 0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests.
  arXiv  Detail & Related papers  (2023-09-01T08:34:13Z)
• Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines [113.08940153125616]
  We generate a dataset of whole-body CT scans with $142$ voxel-level labels for 533 volumes providing comprehensive anatomical coverage. Our proposed procedure does not rely on manual annotation during the label aggregation stage. We release our trained unified anatomical segmentation model capable of predicting $142$ anatomical structures on CT data.
  arXiv  Detail & Related papers  (2023-07-25T09:48:13Z)
• CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline [0.5366236361669898]
  This work proposes a fully automated, and visually explainable, deep learning pipeline to be used as a decision support system for the Coronary Artery Disease screening procedure. The pipeline pre-processes multiplanar projections of the coronary arteries, extracted from the original Coronary Computed Tomography Angiography (CCTA) It is trained to assign a per-patient score by stacking the bi-dimensional longitudinal cross-sections of the three main coronary arteries along channel dimension.
  arXiv  Detail & Related papers  (2023-04-14T17:41:07Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes [59.116811751334225]
  VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans. We extend the labeling mechanism for the cerebral arteries to identify occluded vessels. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
  arXiv  Detail & Related papers  (2022-04-26T14:20:26Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling [5.854918673468336]
  In clinical practice, the severeness of coronary artery disease (CAD) is often assessed with a coronary CT angiography (CCTA) scan. We propose using severity-based label encoding, test time augmentation (TTA) and model ensembling for a task-specific deep learning architecture. We were able to raise the previously reported area under the receiver operating characteristic curve (AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the hold-out task respectively.
  arXiv  Detail & Related papers  (2022-02-08T06:22:03Z)
• 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)
• Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis during CCTA Evaluation of Chest-Pain in the Emergency Department: Preparing an Application for Real-World Use [6.9835031964130545]
  We describe the development of an AI algorithm and workflow for assisting interpreting physicians in CCTA screening for the absence of coronary atherosclerosis. The algorithm demonstrated strong performance with AUC-ROC = 0.96. There is potential for this AI application to assist in CCTA interpretation to help extricate atherosclerosis from chest-pain presentations.
  arXiv  Detail & Related papers  (2020-08-10T16:07:04Z)

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.