Voice-assisted Image Labelling for Endoscopic Ultrasound Classification
using Neural Networks
- URL: http://arxiv.org/abs/2110.06367v1
- Date: Tue, 12 Oct 2021 21:22:24 GMT
- Title: Voice-assisted Image Labelling for Endoscopic Ultrasound Classification
using Neural Networks
- Authors: Ester Bonmati, Yipeng Hu, Alexander Grimwood, Gavin J. Johnson, George
Goodchild, Margaret G. Keane, Kurinchi Gurusamy, Brian Davidson, Matthew J.
Clarkson, Stephen P. Pereira, Dean C. Barratt
- Abstract summary: We propose a multi-modal convolutional neural network architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure.
Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels.
- Score: 48.732863591145964
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ultrasound imaging is a commonly used technology for visualising patient
anatomy in real-time during diagnostic and therapeutic procedures. High
operator dependency and low reproducibility make ultrasound imaging and
interpretation challenging with a steep learning curve. Automatic image
classification using deep learning has the potential to overcome some of these
challenges by supporting ultrasound training in novices, as well as aiding
ultrasound image interpretation in patient with complex pathology for more
experienced practitioners. However, the use of deep learning methods requires a
large amount of data in order to provide accurate results. Labelling large
ultrasound datasets is a challenging task because labels are retrospectively
assigned to 2D images without the 3D spatial context available in vivo or that
would be inferred while visually tracking structures between frames during the
procedure. In this work, we propose a multi-modal convolutional neural network
(CNN) architecture that labels endoscopic ultrasound (EUS) images from raw
verbal comments provided by a clinician during the procedure. We use a CNN
composed of two branches, one for voice data and another for image data, which
are joined to predict image labels from the spoken names of anatomical
landmarks. The network was trained using recorded verbal comments from expert
operators. Our results show a prediction accuracy of 76% at image level on a
dataset with 5 different labels. We conclude that the addition of spoken
commentaries can increase the performance of ultrasound image classification,
and eliminate the burden of manually labelling large EUS datasets necessary for
deep learning applications.
Related papers
- S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography [2.07180164747172]
We introduce an advanced deep learning model, dubbed S-CycleGAN, which generates high-quality synthetic ultrasound images from computed tomography (CT) data.
The synthetic images are utilized to enhance various aspects of our development of the robot-assisted ultrasound scanning system.
arXiv Detail & Related papers (2024-06-03T10:53:45Z) - CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - Cardiac ultrasound simulation for autonomous ultrasound navigation [4.036497185262817]
We propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions.
We present a novel simulation pipeline which uses segmentations from other modalities, an optimized data representation and GPU-accelerated Monte Carlo path tracing.
The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated.
arXiv Detail & Related papers (2024-02-09T15:14:48Z) - 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) - LOTUS: Learning to Optimize Task-based US representations [39.81131738128329]
Anatomical segmentation of organs in ultrasound images is essential to many clinical applications.
Existing deep neural networks require a large amount of labeled data for training in order to achieve clinically acceptable performance.
In this paper, we propose a novel approach for learning to optimize task-based ultra-sound image representations.
arXiv Detail & Related papers (2023-07-29T16:29:39Z) - Data-Efficient Vision Transformers for Multi-Label Disease
Classification on Chest Radiographs [55.78588835407174]
Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images.
ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present.
Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
arXiv Detail & Related papers (2022-08-17T09:07:45Z) - 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) - Semantic segmentation of multispectral photoacoustic images using deep
learning [53.65837038435433]
Photoacoustic imaging has the potential to revolutionise healthcare.
Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information.
We present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images.
arXiv Detail & Related papers (2021-05-20T09:33:55Z) - Ultrasound Image Classification using ACGAN with Small Training Dataset [0.0]
Training deep learning models requires large labeled datasets, which is often unavailable for ultrasound images.
We exploit Generative Adversarial Network (ACGAN) that combines the benefits of large data augmentation and transfer learning.
We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.
arXiv Detail & Related papers (2021-01-31T11:11:24Z) - Breast lesion segmentation in ultrasound images with limited annotated
data [2.905751301655124]
We propose the use of simulated US images and natural images as auxiliary datasets in order to pre-train our segmentation network.
We show that fine-tuning the pre-trained network improves the dice score by 21% compared to training from scratch.
arXiv Detail & Related papers (2020-01-21T03:34:42Z)
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.