Dr. Shun Miao Profile

Dr. Shun Miao

at Siemens Healthineers

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Publications (2)

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SPIE Journal Paper | 13 January 2018

Pairwise domain adaptation module for CNN-based 2-D/3-D registration

Jiannan Zheng, Shun Miao, Z. Jane Wang, Rui Liao

JMI, Vol. 5, Issue 02, 021204, (January 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.2.021204

KEYWORDS: Personal digital assistants, Data modeling, Image registration, X-ray imaging, X-rays, Spine, 3D image processing, Performance modeling, 3D modeling, Transducers

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Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D registration formulate the problem as a learning-based approach and exploit the modeling power of convolutional neural networks (CNN) to significantly improve the accuracy and efficiency of 2-D/3-D registration. However, for surgery-related applications, collecting a large clinical dataset with accurate annotations for training can be very challenging or impractical. Therefore, deep learning-based 2-D/3-D registration methods are often trained with synthetically generated data, and a performance gap is often observed when testing the trained model on clinical data. We propose a pairwise domain adaptation (PDA) module to adapt the model trained on source domain (i.e., synthetic data) to target domain (i.e., clinical data) by learning domain invariant features with only a few paired real and synthetic data. The PDA module is designed to be flexible for different deep learning-based 2-D/3-D registration frameworks, and it can be plugged into any pretrained CNN model such as a simple Batch-Norm layer. The proposed PDA module has been quantitatively evaluated on two clinical applications using different frameworks of deep networks, demonstrating its significant advantages of generalizability and flexibility for 2-D/3-D medical image registration when a small number of paired real-synthetic data can be obtained.

Proceedings Article | 17 February 2012 Paper

Automatic pose initialization for accurate 2D/3D registration applied to abdominal aortic aneurysm endovascular repair

Shun Miao, Joseph Lucas, Rui Liao

Proceedings Volume 8316, 83160Q (2012) https://doi.org/10.1117/12.911495

KEYWORDS: Spine, Image registration, X-ray imaging, X-rays, 3D image processing, Arteries, 3D modeling, Image enhancement, Opacity, Image filtering

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Minimally invasive abdominal aortic aneurysm (AAA) stenting can be greatly facilitated by overlaying the preoperative 3-D model of the abdominal aorta onto the intra-operative 2-D X-ray images. Accurate 2-D/3-D registration in 3-D space makes the 2-D/3-D overlay robust to the change of C-Arm angulations. By far, the 2-D/3-D registration methods based on simulated X-ray projection images using multiple image planes have been shown to be able to provide satisfactory 3-D registration accuracy. However, one drawback of the intensity-based 2-D/3-D registration methods is that the similarity measure is usually highly non-convex and hence the optimizer can easily be trapped into local minima. User interaction therefore is often needed in the initialization of the position of the 3-D model in order to get a successful 2-D/3-D registration. In this paper, a novel 3-D pose initialization technique is proposed, as an extension of our previously proposed bi-plane 2-D/3-D registration method for AAA intervention [4]. The proposed method detects vessel bifurcation points and spine centerline in both 2-D and 3-D images, and utilizes landmark information to bring the 3-D volume into a 15mm capture range. The proposed landmark detection method was validated on real dataset, and is shown to be able to provide a good initialization for 2-D/3-D registration in [4], thus making the workflow fully automatic.

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