Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection

• URL: http://arxiv.org/abs/2203.10039v1
• Date: Fri, 18 Mar 2022 16:26:53 GMT
• Title: Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
• Authors: Luca Tomasetti, Mahdieh Khanmohammadi, Kjersti Engan, Liv Jorunn H{\o}llesli, and Kathinka D{\ae}hli Kurz
• Abstract summary: We propose an automatic method to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke. Our model is based on a convolution-deconvolution bottleneck structure with multi-input and slow fusion. The proposed architecture demonstrates effective performance and results comparable to the ground truth annotated by neuroradiologists.
• Score: 1.372466817835681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time is a fundamental factor during stroke treatments. A fast, automatic approach that segments the ischemic regions helps treatment decisions. In clinical use today, a set of color-coded parametric maps generated from computed tomography perfusion (CTP) images are investigated manually to decide a treatment plan. We propose an automatic method based on a neural network using a set of parametric maps to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke. Our model is based on a convolution-deconvolution bottleneck structure with multi-input and slow fusion. A loss function based on the focal Tversky index addresses the data imbalance issue. The proposed architecture demonstrates effective performance and results comparable to the ground truth annotated by neuroradiologists. A Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel occlusion test set is achieved. The full implementation is available at: https://git.io/JtFGb.

Related papers

• Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph [49.11220791279602]
  It is challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications. A graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup.
  arXiv  Detail & Related papers  (2023-07-07T18:57:21Z)
• CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke [1.6836876499886009]
  This paper investigates different methods to utilize the entire 4 convolutionD as input to fully exploit thetemporal information. Adopting the proposed 4D mJ-Net, a Dice Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core areas, respectively.
  arXiv  Detail & Related papers  (2023-03-15T16:53:19Z)
• UniToBrain dataset: a Brain Perfusion Dataset [2.02258267891574]
  We present UniToBrain, the very first open-source dataset for "perfusion maps" We propose a novel neural networks-based algorithm, using the European library ECVL and EDDL for the image processing and developing deep learning models respectively.
  arXiv  Detail & Related papers  (2022-08-01T07:16:02Z)
• 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)
• 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)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• 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)
• DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation [0.0]
  We propose a novel Dimension Fusion Edge-guided network (DFENet) that can meet both of these requirements by fusing the features of 2D and 3D CNNs. The proposed model is robust, accurate, superior to the existing methods, and can be relied upon for biomedical applications.
  arXiv  Detail & Related papers  (2021-05-17T15:43:59Z)
• CNN Based Segmentation of Infarcted Regions in Acute Cerebral Stroke Patients From Computed Tomography Perfusion Imaging [2.1626699124055504]
  Thrombolytic treatment can reduce brain damage but has a narrow treatment window. Computed To Perfusion imaging is a commonly used primary assessment tool for stroke patients. We propose a fully automated four-dimensional convolutional neural network based segmentation method.
  arXiv  Detail & Related papers  (2021-04-07T09:09:13Z)
• Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke [4.925222726301579]
  Convolutional Neural Network (CNN) can generate clinically relevant parametric maps from CT perfusion data. Our CNN-based approach generated clinically relevant perfusion maps that are comparable to state-of-the-art perfusion analysis methods.
  arXiv  Detail & Related papers  (2021-01-15T07:11:02Z)
• 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)

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.