Automated Guidance for Fetal Brain Ultrasound Scanning: Detecting Proximity to Standard Planes

Temel Kavramlar

This paper introduces a novel pipeline that combines semi-supervised segmentation and classification of fetal brain images with sensorless proximity detection to the transventricular standard plane, aiming to assist sonographers in navigating to and reproducing standard imaging planes during fetal ultrasound scanning.

Özet

The paper presents a pipeline for fetal brain ultrasound analysis that consists of three main steps:

Detection and segmentation of the fetal brain in ultrasound images using a semi-supervised segmentation and classification model (SS-Seg+Class). This model is trained on a combination of labeled standard plane images and unlabeled 3D ultrasound volume slices, enabling reliable segmentation across a diverse set of fetal brain images.

Plane pose regression on the masked fetal brain images using a residual convolutional neural network (ResNet-18). This model predicts the 6D pose of the ultrasound plane with respect to the fetal brain.

Measurement of proximity to the target transventricular (TV) standard plane. The authors manually annotate the canonical pose of the TV standard plane on the training 3D ultrasound volumes and use this information to estimate the relative proximity to this plane during inference.

The authors validate their pipeline on recorded videos of real fetal ultrasound scans from sonographers with varying expertise. The results demonstrate that the proximity predictions from the model tend to align with expert assessment of standard plane quality, indicating the potential of the approach to assist sonographers in identifying high-quality standard planes during scanning.
The authors discuss the limitations of the current model, such as sensitivity to factors like zooming and off-center anatomies, and suggest future work to improve robustness and extend the approach to other standard planes or anatomical regions.

İstatistikler

The paper reports the following key metrics:

Mean Intersection over Union (mIoU) on labeled test set: 0.9482
Mean IoU between pairs of slices at the same pose from the validation sets: 0.8278
Average translation error: 3.35 mm
Average rotation error: 6.96 degrees

Alıntılar

"Our SS-Seg+Class model shows robustness on both labeled and unlabeled data. Our model outperforms the others in segmenting SPs and non-SPs with improved mean IoU scores across images of 0.9482 on labeled images and 0.8272 on unlabeled images thanks to the inter-patient loss function."
"The predicted distances to the SP, along with SonoNet's predicted labels, offer insights into the model's precision in identifying and segmenting fetal brain structures."

Önemli Bilgiler Şuradan Elde Edildi

Measuring proximity to standard planes during fetal brain ultrasound scanning

by Chiara Di Ve... : arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07124.pdf

Daha Derin Sorular

How can the proposed pipeline be extended to provide guidance for other standard planes in the fetal anatomy beyond the transventricular plane?

The proposed pipeline can be extended to provide guidance for other standard planes in the fetal anatomy by incorporating additional training data specific to those planes. This would involve acquiring labeled images of the desired standard planes and integrating them into the training process of the segmentation and classification model. By expanding the dataset to include a variety of standard planes, the model can learn to accurately identify and segment different anatomical structures, enabling it to provide guidance for a broader range of standard planes in fetal anatomy.

What are the potential challenges in adapting the model to work with real-time ultrasound data and provide immediate feedback to the sonographer during the scanning process?

Adapting the model to work with real-time ultrasound data and provide immediate feedback to the sonographer during the scanning process poses several challenges. One major challenge is the need for real-time processing capabilities to analyze ultrasound frames quickly and accurately. This requires optimizing the model for efficiency without compromising on accuracy. Additionally, ensuring seamless integration of the model into the ultrasound machine's software and hardware systems is crucial for real-time feedback delivery. Another challenge is handling the variability and noise present in live ultrasound data, which may impact the model's performance and reliability in providing immediate feedback to the sonographer.

How could the integration of temporal information from the ultrasound video feed further improve the accuracy and robustness of the plane pose regression and proximity detection?

Integrating temporal information from the ultrasound video feed can enhance the accuracy and robustness of the plane pose regression and proximity detection in several ways. By analyzing sequential frames over time, the model can leverage temporal consistency to improve the tracking of standard planes and fetal structures. This temporal context can help in predicting the movement and orientation of the ultrasound probe, aiding in more accurate pose regression. Additionally, incorporating temporal information can enable the model to account for dynamic changes in fetal anatomy during scanning, leading to more reliable proximity detection to standard planes. Overall, the integration of temporal information can enhance the model's performance in real-time ultrasound analysis by capturing the dynamic nature of the scanning process.