RecON: Online learning for sensorless freehand 3D ultrasound reconstruction

Luo, Mingyuan; Wang, Hongzhang; Dou, Haoran; Hu, Xindi; Huang, Yuhao; Ravikumar, Nishant; Xu, Songcheng; Zhang, Yuanji; Xiong, Yi; Xue, Wufeng; Frangi, Alejandro F.; Ni, Dong; Sun, Lei

doi:10.1016/j.media.2023.102810

Cited by 13 publications

(1 citation statement)

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“…Other than using a 3D ultrasound transducer that could acquire a 3D surface directly, intraoperative 2D ultrasound imaging can also reconstruct 3D models with known spatial information of the scan slice as illustrated in Figure 5 Reconstruction can subsequently be conducted after segmentation of the 3D surface based on the intensity of the ultrasonography, as illustrated in the same figure showcasing the 3D reconstruction of a placenta in a fluid medium [ 120 ]. Recent work in relation to 3D ultrasound reconstruction has also explored promising machine learning-based approaches [ 121 , 122 ]. Endoscopic camera-based image reconstruction is another commonly used approach for intraoperative reconstruction of 3D structures in the scene [ 123 , 124 ].…”

Section: Resultsmentioning

confidence: 99%

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization

Lin,

Lei,

Yang

2023

Sensors

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Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functionality breakthroughs. However, with the overwhelming pool of information in the literature, it has become increasingly challenging for researchers to extract context-relevant information for specific applications, especially when many widely used methods exist in a variety of versions optimized for their respective application domains. By being further equipped with sophisticated three-dimensional (3D) medical image visualization and digital reality technology, medical experts could enhance their performance capabilities in IGS by multiple folds. The goal of this narrative review is to organize the key components of IGS in the aspects of medical image processing and visualization with a new perspective and insights. The literature search was conducted using mainstream academic search engines with a combination of keywords relevant to the field up until mid-2022. This survey systemically summarizes the basic, mainstream, and state-of-the-art medical image processing methods as well as how visualization technology like augmented/mixed/virtual reality (AR/MR/VR) are enhancing performance in IGS. Further, we hope that this survey will shed some light on the future of IGS in the face of challenges and opportunities for the research directions of medical image processing and visualization.

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