Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography
- URL: http://arxiv.org/abs/2603.27143v1
- Date: Sat, 28 Mar 2026 05:34:48 GMT
- Title: Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography
- Abstract summary: Apical 4-chamber (A4CH) view is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF)<n>We present a multi-task network that provides feedback cues for A4CH view acquisition and automatically estimates LVEF in high-quality A4CH images.
- Score: 7.643959106718679
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Point-of-care transthoracic echocardiography (TTE) makes it possible to assess a patient's cardiac function in almost any setting. A critical step in the TTE exam is acquisition of the apical 4-chamber (A4CH) view, which is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF). However, optimizing transducer pose for high-quality image acquisition and subsequent measurement is a challenging task, particularly for novice users. In this work, we present a multi-task network that provides feedback cues for A4CH view acquisition and automatically estimates LVEF in high-quality A4CH images. The network cascades a transducer pose scoring module and an uncertainty-aware LV landmark detector with automated LVEF estimation. A strength is that network training and inference do not require cumbersome or costly setups for transducer position tracking. We evaluate performance on point-of-care TTE data acquired with a spatially dense "sweep" protocol around the optimal A4CH view. The results demonstrate the network's ability to determine when the transducer pose is on target, close to target, or far from target based on the images alone, while generating visual landmark cues that guide anatomical interpretation and orientation. In conclusion, we demonstrate a promising strategy to provide guidance for A4CH view acquisition, which may be useful when deploying point-of-care TTE in limited resource settings.
Related papers
- EchoVQA: Enabling Conversational Assistance for Point-of-Care Cardiac Ultrasound [18.04473134587132]
We introduce EchoVQA, the first large-scale VQA dataset for echocardiography, comprising 14,299 images and 74,819 question-answer pairs.<n> EchoVQA includes acquisition guidance questions to help users optimize transducer positioning toward a diagnostic apical 4-chamber view for left ventricular ejection fraction estimation.
arXiv Detail & Related papers (2026-05-22T19:28:45Z) - Think as Cardiac Sonographers: Marrying SAM with Left Ventricular Indicators Measurements According to Clinical Guidelines [10.334018181732022]
Left ventricular (LV) indicator measurements following clinical echocardiog-raphy guidelines are important for diagnosing cardiovascular disease.<n>It is necessary to introduce vision founda-tional models (VFM) with abundant knowledge.<n>We propose a novel framework named AutoSAME, combining the powerful visual understanding of SAM with seg-mentation and landmark localization tasks simultaneously.
arXiv Detail & Related papers (2025-08-12T02:09:36Z) - Real-time guidewire tracking and segmentation in intraoperative x-ray [52.51797358201872]
We propose a two-stage deep learning framework for real-time guidewire segmentation and tracking.
In the first stage, a Yolov5 detector is trained, using the original X-ray images as well as synthetic ones, to output the bounding boxes of possible target guidewires.
In the second stage, a novel and efficient network is proposed to segment the guidewire in each detected bounding box.
arXiv Detail & Related papers (2024-04-12T20:39:19Z) - GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection [51.43589678946244]
This paper explores the potential of VQA-oriented GPT-4V in the popular visual Anomaly Detection (AD) task.
It is the first to conduct qualitative and quantitative evaluations on the popular MVTec AD and VisA datasets.
arXiv Detail & Related papers (2023-11-05T10:01:18Z) - A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical
Image Analysis [87.25494411021066]
GPT-4V's multimodal capability for medical image analysis is evaluated.
It is found that GPT-4V excels in understanding medical images and generates high-quality radiology reports.
It is found that its performance for medical visual grounding needs to be substantially improved.
arXiv Detail & Related papers (2023-10-31T11:39:09Z) - Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation [19.455350961592742]
We propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps.
FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling.
We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF.
arXiv Detail & Related papers (2023-09-07T07:46:46Z) - EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark
Detection on Echocardiograms [21.537833605289816]
We introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD)
Our main contributions are 1) a hierarchical graph representation learning framework for multi-resolution landmark detection via GNNs; 2) induced hierarchical supervision at different levels of granularity using a multi-level loss.
arXiv Detail & Related papers (2023-07-23T05:31:47Z) - 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) - Superresolution and Segmentation of OCT scans using Multi-Stage
adversarial Guided Attention Training [18.056525121226862]
We propose the multi-stage & multi-discriminatory generative adversarial network (MultiSDGAN) to translate OCT scans in high-resolution segmentation labels.
We evaluate and compare various combinations of channel and spatial attention to the MultiSDGAN architecture to extract more powerful feature maps.
Our results demonstrate relative improvements of 21.44% and 19.45% on the Dice coefficient and SSIM, respectively.
arXiv Detail & Related papers (2022-06-10T00:26:55Z) - Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
We propose a new framework called Ventral-Dorsal Networks (VDNets)
Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network"
Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
arXiv Detail & Related papers (2020-05-15T23:57:36Z) - AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement
with Neural Searching [76.4844593082362]
We investigate the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong baseline for remote HR measurement with architecture search (NAS)
Comprehensive experiments are performed on three benchmark datasets on both intra-temporal and cross-dataset testing.
arXiv Detail & Related papers (2020-04-26T05:43:21Z) - Volumetric Attention for 3D Medical Image Segmentation and Detection [53.041572035020344]
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed.
VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction.
Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor (LiTS) Challenge.
arXiv Detail & Related papers (2020-04-04T18:55:06Z)
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.