An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery

• URL: http://arxiv.org/abs/2304.05463v2
• Date: Thu, 6 Jul 2023 17:29:25 GMT
• Title: An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery
• Authors: Chun Kit Wong and Manxi Lin and Alberto Raheli and Zahra Bashir and Morten Bo S{\o}ndergaard Svendsen and Martin Gr{\o}nneb{\ae}k Tolsgaard and Aasa Feragen and Anders Nymark Christensen
• Abstract summary: A shortage of experienced sonographers has created a demand for machine assistance. In this work, we propose an automatic system to fill the gap. The proposed system is validated on 657 images from a national ultrasound screening database.
• Score: 2.4626113631507893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health. Such examination involves several steps that must be performed correctly: identifying suitable sites on the umbilical artery for the measurement, acquiring the blood flow curve in the form of a Doppler spectrum, and ensuring compliance to a set of quality standards. These steps rely heavily on the operator's skill, and the shortage of experienced sonographers has thus created a demand for machine assistance. In this work, we propose an automatic system to fill the gap. By using a modified Faster R-CNN network, we obtain an algorithm that can suggest locations suitable for Doppler measurement. Meanwhile, we have also developed a method for assessment of the Doppler spectrum's quality. The proposed system is validated on 657 images from a national ultrasound screening database, with results demonstrating its potential as a guidance system.

Related papers

• Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning [11.137087573421258]
  We propose an automatic three-dimensional multi-modal (B-mode and power Doppler) ultrasound segmentation of the human placenta. We collected data containing B-mode and power Doppler ultrasound scans for 400 studies. We found that multimodal information in the form of B-mode and power Doppler scans outperform any single modality.
  arXiv  Detail & Related papers  (2024-01-17T23:17:08Z)
• A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography [3.7775754350457746]
  We introduce a novel unified framework using a convolutional neural network for comprehensive analysis of spectral and tissue Doppler echocardiography images. The network automatically recognizes key features across various Doppler views, with novel Doppler shape embedding and anti-aliasing modules. Empirical results indicate a consistent outperformance in performance metrics, including dice similarity coefficients (DSC) and intersection over union (IoU)
  arXiv  Detail & Related papers  (2023-11-14T15:10:05Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal [48.97719097435527]
  DopUS-Net combines the Doppler images with B-mode images to increase the segmentation accuracy and robustness of small blood vessels. An artery re-identification module qualitatively evaluate the real-time segmentation results and automatically optimize the probe pose for enhanced Doppler images.
  arXiv  Detail & Related papers  (2023-05-15T18:19:29Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• 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)
• Deep Learning for Ultrasound Beamforming [120.12255978513912]
  Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain. Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Deep learning methods can play a compelling role in the digital beamforming pipeline.
  arXiv  Detail & Related papers  (2021-09-23T15:15:21Z)
• FetalNet: Multi-task deep learning framework for fetal ultrasound biometric measurements [11.364211664829567]
  We propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for fetal ultrasound scan video analysis. The main goal in fetal ultrasound video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Our method called FetalNet outperforms existing state-of-the-art methods in both classification and segmentation in fetal ultrasound video recordings.
  arXiv  Detail & Related papers  (2021-07-14T19:13:33Z)
• AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes [10.745788530692305]
  The proposed framework semantically segments the key fetal anatomies using state-of-the-art segmentation models. We show that the network with the best segmentation performance tends to be more accurate for biometry estimation.
  arXiv  Detail & Related papers  (2021-07-12T08:42:31Z)
• Noise-Resilient Automatic Interpretation of Holter ECG Recordings [67.59562181136491]
  We present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with gradientdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage is a boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features.
  arXiv  Detail & Related papers  (2020-11-17T16:15:49Z)
• Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening [7.496518691842825]
  We propose a pipeline for automated data curation and classification. We exploit an auxiliary view classification task to bias features toward relevant cardiac structures. This bias helps to improve in F1-scores from 0.72 and 0.77 to 0.87 and 0.85 for healthy and CHD classes respectively.
  arXiv  Detail & Related papers  (2020-08-16T17:53:41Z)

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.