Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings最新文献

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Coordinate Translator for Learning Deformable Medical Image Registration 坐标翻译学习变形医学图像配准

Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings

Pub Date : 2022-03-05 DOI: 10.48550/arXiv.2203.03626

Yihao Liu, Lianrui Zuo, Shuo Han, Jerry L Prince, A. Carass

The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between the fixed and moving image and outputs their coordinate correspondences without the need for training. It unloads the burden of understanding image coordinate systems for CNNs, allowing them to focus on feature extraction. We then propose a novel deformable registration network, im2grid, that uses multiple Coordinate Translator's with the hierarchical features extracted from a CNN encoder and outputs a deformation field in a coarse-to-fine fashion. We compared im2grid with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic resonance image registration. Our experiments show that im2grid outperforms these methods both qualitatively and quantitatively.

大多数基于深度学习(DL)的可变形图像配准方法使用卷积神经网络(cnn)来估计运动和固定图像对的位移场。然而，这要求CNN中的卷积核不仅要从输入中提取强度特征，还要理解图像坐标系。我们认为后一项任务对传统cnn来说是一个挑战，限制了它们在注册任务中的表现。为了解决这个问题，我们首先引入了坐标转换器，这是一个可微模块，可以识别固定图像和运动图像之间的匹配特征，并在不需要训练的情况下输出它们的坐标对应。它为cnn减轻了理解图像坐标系统的负担，使他们能够专注于特征提取。然后，我们提出了一种新的可变形配准网络im2grid，它使用多个坐标转换器和从CNN编码器提取的分层特征，并以粗到细的方式输出变形场。我们将im2grid与最先进的深度学习和非深度学习方法进行了比较，用于无监督的3D磁共振图像配准。我们的实验表明，im2grid在定性和定量上都优于这些方法。

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引用次数: 14

Multiscale Multimodal Medical Imaging: Third International Workshop, MMMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 多尺度多模式医学成像:第三届国际研讨会，MMMI 2022，与MICCAI 2022一起举行，新加坡，2022年9月22日，会议录

Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings

Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-18814-5

引用次数: 0

Multiscale Multimodal Medical Imaging: First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 多尺度多模态医学成像:首届国际研讨会，MMMI 2019，与MICCAI 2019一起举行，中国深圳，2019年10月13日，会议录

Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings

Pub Date : 2020-01-01 DOI: 10.1007/978-3-030-37969-8

Ning Qiang, Bao Ge, Qinglin Dong, Fangfei Ge, Tianming Liu

引用次数: 2

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