Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction

TL;DR

This paper introduces a novel online learning framework with self-supervised and adversarial training for sensorless freehand 3D ultrasound reconstruction, improving robustness and accuracy in complex clinical scenarios.

Contribution

It proposes an end-to-end differentiable reconstruction algorithm, self-supervised context learning, and shape prior adversarial training for better handling diverse skill sequences.

Findings

Outperforms state-of-the-art methods in shift errors.

Achieves higher path similarity in reconstructions.

Effective on fetal and hip ultrasound datasets.

Abstract

3D ultrasound (US) is widely used for its rich diagnostic information. However, it is criticized for its limited field of view. 3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. The existing deep learning based methods only focus on the basic cases of skill sequences, and the model relies on the training data heavily. The sequences in real clinical practice are a mix of diverse skills and have complex scanning paths. Besides, deep models should adapt themselves to the testing cases with prior knowledge for better robustness, rather than only fit to the training cases. In this paper, we propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences. Our contribution is three-fold. First, we advance a novel online learning framework by designing a differentiable reconstruction algorithm. It realizes an end-to-end optimization from section sequences to the reconstructed volume. Second, a self-supervised learning method is developed to explore the context information that reconstructed by the testing data itself, promoting the perception of the model. Third, inspired by the effectiveness of shape prior, we also introduce adversarial training to strengthen the learning of anatomical shape prior in the reconstructed volume. By mining the context and structural cues of the testing data, our online learning methods can drive the model to handle complex skill sequences. Experimental results on developmental dysplasia of the hip US and fetal US datasets show that, our proposed method can outperform the start-of-the-art methods regarding the shift errors and path similarities.