AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning

• URL: http://arxiv.org/abs/2010.01617v1
• Date: Sun, 4 Oct 2020 16:14:45 GMT
• Title: AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
• Authors: Ezequiel de la Rosa, Diana M. Sima, Bjoern Menze, Jan S. Kirschke, David Robben
• Abstract summary: Deconvolution methods are used to obtain clinically interpretable perfusion parameters. AIFNet is a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. We conclude that AIFNet has potential for clinical transfer and could be incorporated in deconvolution software.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.

Related papers

• KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
  We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-10-28T16:00:42Z)
• KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation [51.03868117057726]
  This paper proposes a novel Kalman filter based Linear Deformable Diffusion (KLDD) model for retinal vessel segmentation. Our model employs a diffusion process that iteratively refines the segmentation, leveraging the flexible receptive fields of deformable convolutions. Experiments are evaluated on retinal fundus image datasets (DRIVE, CHASE_DB1) and the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-09-19T14:21:38Z)
• Detection of the Arterial Input Function Using DSC-MRI Data [0.0]
  Accurate detection of arterial input function is a crucial step in obtaining perfusion hemodynamic parameters. Several methods have been proposed to improve the reliability of arterial input function detection. Methods most commonly used for semi- and fully automatic arterial input function detection are reviewed.
  arXiv  Detail & Related papers  (2023-03-04T22:30:38Z)
• Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases [0.0]
  This study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. We have proposed a clinician-in-the-loop assisted intelligent workflow that performs a retinal vascular assessment on the fundus images. The semiautomatic methodology aims to have a federated approach to AI in healthcare applications with more inputs and interpretations from clinicians.
  arXiv  Detail & Related papers  (2022-12-14T07:00:31Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation [7.670940374203646]
  Two common vascular features, vessel density and fractal dimension, are identified to be sensitive to intra-segment misclassification. We show that incorporating our end-to-end VAFO-Loss in standard segmentation networks indeed improves vascular feature estimation. We also report a technically interesting finding that the trained segmentation network, albeit biased by the feature optimised loss VAFO-Loss, shows statistically significant improvement in segmentation metrics.
  arXiv  Detail & Related papers  (2022-03-12T12:40:20Z)
• 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)
• Differentiable Deconvolution for Improved Stroke Perfusion Analysis [0.0]
  arterial input function (AIF) is still controversial how and where it should be chosen. We propose an AIF selection approach that is optimized for maximal core lesion segmentation performance. We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
  arXiv  Detail & Related papers  (2021-03-31T14:29:36Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)

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.