Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates
in the Fetal Brain
- URL: http://arxiv.org/abs/2301.08317v2
- Date: Thu, 2 Nov 2023 13:08:25 GMT
- Title: Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates
in the Fetal Brain
- Authors: Chiara Di Vece, Maela Le Lous, Brian Dromey, Francisco Vasconcelos,
Anna L David, Donald Peebles, Danail Stoyanov
- Abstract summary: We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors.
This work builds on our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain.
We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models.
- Score: 9.465965149145559
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In obstetric ultrasound (US) scanning, the learner's ability to mentally
build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US
image represents a significant challenge in skill acquisition. We aim to build
a US plane localization system for 3D visualization, training, and guidance
without integrating additional sensors. This work builds on top of our previous
work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US
planes slicing the fetal brain with respect to a normalized reference frame
using a convolutional neural network (CNN) regression network. Here, we analyze
in detail the assumptions of the normalized fetal brain reference frame and
quantify its accuracy with respect to the acquisition of transventricular (TV)
standard plane (SP) for fetal biometry. We investigate the impact of
registration quality in the training and testing data and its subsequent effect
on trained models. Finally, we introduce data augmentations and larger training
sets that improve the results of our previous work, achieving median errors of
2.97 mm and 6.63 degrees for translation and rotation, respectively.
Related papers
- Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos [0.8579241568505183]
We propose an uncertainty-aware deep learning model for automated 3D plane localization in 2D fetal brain images.
Our proposed method, QAERTS, demonstrates superior pose estimation accuracy than the state-of-the-art and most of the uncertainty-based approaches.
arXiv Detail & Related papers (2024-05-21T22:42:08Z) - Measuring proximity to standard planes during fetal brain ultrasound scanning [8.328549443700858]
This paper introduces a novel pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use.
We propose a semi-supervised segmentation model utilizing both labeled SPs and unlabeled 3D US volume slices.
Our model enables reliable segmentation across a diverse set of fetal brain images.
arXiv Detail & Related papers (2024-04-10T16:04:21Z) - FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound [28.408626329596668]
We propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges.
First, we propose a scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches.
Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures.
Third, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online.
arXiv Detail & Related papers (2023-10-30T06:18:47Z) - Video Pretraining Advances 3D Deep Learning on Chest CT Tasks [63.879848037679224]
Pretraining on large natural image classification datasets has aided model development on data-scarce 2D medical tasks.
These 2D models have been surpassed by 3D models on 3D computer vision benchmarks.
We show video pretraining for 3D models can enable higher performance on smaller datasets for 3D medical tasks.
arXiv Detail & Related papers (2023-04-02T14:46:58Z) - Agent with Tangent-based Formulation and Anatomical Perception for
Standard Plane Localization in 3D Ultrasound [56.7645826576439]
We introduce a novel reinforcement learning framework for automatic SP localization in 3D US.
First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space.
Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search.
arXiv Detail & Related papers (2022-07-01T14:53:27Z) - Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound
Reconstruction [61.62191904755521]
3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan.
Existing deep learning based methods only focus on the basic cases of skill sequences.
We propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
arXiv Detail & Related papers (2021-07-31T16:06:50Z) - Revisiting 3D Context Modeling with Supervised Pre-training for
Universal Lesion Detection in CT Slices [48.85784310158493]
We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices.
With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset.
The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
arXiv Detail & Related papers (2020-12-16T07:11:16Z) - Cephalometric Landmark Regression with Convolutional Neural Networks on
3D Computed Tomography Data [68.8204255655161]
Cephalometric analysis performed on lateral radiographs doesn't fully exploit the structure of 3D objects due to projection onto the lateral plane.
We present a series of experiments with state of the art 3D convolutional neural network (CNN) based methods for keypoint regression.
For the first time, we extensively evaluate the described methods and demonstrate their effectiveness in the estimation of Frankfort Horizontal and cephalometric points locations.
arXiv Detail & Related papers (2020-07-20T12:45:38Z) - Region Proposal Network with Graph Prior and IoU-Balance Loss for
Landmark Detection in 3D Ultrasound [16.523977092204813]
3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring.
To analyze a 3D US volume, it is fundamental to identify anatomical landmarks accurately.
We exploit an object detection framework to detect landmarks in 3D fetal facial US volumes.
arXiv Detail & Related papers (2020-04-01T03:00:03Z) - FetusMap: Fetal Pose Estimation in 3D Ultrasound [42.59502360552173]
We propose to estimate the 3D pose of fetus in US volumes to facilitate its quantitative analyses.
This is the first work about 3D pose estimation of fetus in the literature.
We propose a self-supervised learning (SSL) framework to finetune the deep network to form visually plausible pose predictions.
arXiv Detail & Related papers (2019-10-11T01:45:09Z) - Agent with Warm Start and Active Termination for Plane Localization in
3D Ultrasound [56.14006424500334]
Standard plane localization is crucial for ultrasound (US) diagnosis.
In prenatal US, dozens of standard planes are manually acquired with a 2D probe.
We propose a novel reinforcement learning framework to automatically localize fetal brain standard planes in 3D US.
arXiv Detail & Related papers (2019-10-10T02:21:52Z)
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.