EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from
Spatiotemporal Echocardiography
- URL: http://arxiv.org/abs/2209.04242v1
- Date: Fri, 9 Sep 2022 11:01:59 GMT
- Title: EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from
Spatiotemporal Echocardiography
- Authors: Rand Muhtaseb and Mohammad Yaqub
- Abstract summary: We propose a method that addresses the limitations we typically face when training on medical video data such as echocardiographic scans.
The algorithm we propose (EchoTr) utilizes the strength of vision transformers and CNNs to tackle the problem of estimating the left ventricular ejection fraction (LVEF) on ultrasound videos.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Learning spatiotemporal features is an important task for efficient video
understanding especially in medical images such as echocardiograms.
Convolutional neural networks (CNNs) and more recent vision transformers (ViTs)
are the most commonly used methods with limitations per each. CNNs are good at
capturing local context but fail to learn global information across video
frames. On the other hand, vision transformers can incorporate global details
and long sequences but are computationally expensive and typically require more
data to train. In this paper, we propose a method that addresses the
limitations we typically face when training on medical video data such as
echocardiographic scans. The algorithm we propose (EchoCoTr) utilizes the
strength of vision transformers and CNNs to tackle the problem of estimating
the left ventricular ejection fraction (LVEF) on ultrasound videos. We
demonstrate how the proposed method outperforms state-of-the-art work to-date
on the EchoNet-Dynamic dataset with MAE of 3.95 and $R^2$ of 0.82. These
results show noticeable improvement compared to all published research. In
addition, we show extensive ablations and comparisons with several algorithms,
including ViT and BERT. The code is available at
https://github.com/BioMedIA-MBZUAI/EchoCoTr.
Related papers
- Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets [2.0286377328378737]
Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases.
In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed.
arXiv Detail & Related papers (2024-04-30T14:16:45Z) - 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) - SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning [0.8672882547905405]
We develop SimLVSeg, a video-based network for consistent left ventricular (LV) segmentation from sparsely annotated echocardiogram videos.
SimLVSeg consists of self-supervised pre-training with temporal masking, followed by weakly supervised learning tailored for LV segmentation from sparse annotations.
We demonstrate how SimLVSeg outperforms the state-of-the-art solutions by achieving a 93.32% dice score on the largest 2D+time echocardiography dataset.
arXiv Detail & Related papers (2023-09-30T18:13:41Z) - 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) - D'ARTAGNAN: Counterfactual Video Generation [3.4079278794252232]
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions.
We combine deep neural networks, twin causal networks and generative adversarial methods for the first time to build D'ARTAGNAN.
We generate new ultrasound videos, retaining the video style and anatomy of the original patient, with variations of the Ejection Fraction conditioned on a given input.
arXiv Detail & Related papers (2022-06-03T15:53:32Z) - 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) - Voice-assisted Image Labelling for Endoscopic Ultrasound Classification
using Neural Networks [48.732863591145964]
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.
arXiv Detail & Related papers (2021-10-12T21:22:24Z) - Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for
Video Segmentation and Myocardial Infarction Detection in Echocardiography [7.378083964709321]
We propose an innovative real-time end-to-end fully automated model based on convolutional neural networks (CNN)
Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI.
arXiv Detail & Related papers (2021-03-26T21:03:33Z) - Weakly-supervised Learning For Catheter Segmentation in 3D Frustum
Ultrasound [74.22397862400177]
We propose a novel Frustum ultrasound based catheter segmentation method.
The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
arXiv Detail & Related papers (2020-10-19T13:56:22Z) - 3D medical image segmentation with labeled and unlabeled data using
autoencoders at the example of liver segmentation in CT images [58.720142291102135]
This work investigates the potential of autoencoder-extracted features to improve segmentation with a convolutional neural network.
A convolutional autoencoder was used to extract features from unlabeled data and a multi-scale, fully convolutional CNN was used to perform the target task of 3D liver segmentation in CT images.
arXiv Detail & Related papers (2020-03-17T20:20:43Z)
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.