Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors

Wei, Dongming; Ahmad, Sahar; Huo, Jiayu; Wen, Peng; Ge, Yunhao; Zhang, Xue; Yap, Pew Thian; Li, Wentao; Shen, Dinggang; Wang, Qian

doi:10.1007/978-3-030-32254-0_57

Cited by 27 publications

(29 citation statements)

References 13 publications

(15 reference statements)

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“…If not considered carefully, the fused deformation may otherwise yield performance degradation. As for the dual-branch deformation fusion, the most related approach [14] uses average operation. Hard-coding average operation considers two deformations equally, which may disregard the relative importance of each voxel from two distinct streams.…”

Section: Gated Dual-branch Fusion Modulementioning

confidence: 99%

Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

Yan

Luo

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Multimodal image registration is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.

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Section: Gated Dual-branch Fusion Modulementioning

confidence: 99%

Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

Yan

Luo

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…The first dataset used in this study come from a liver 1 http://www.imagecomputing.org/~cmli/DRLSE/ 2 https://jp.mathworks.com/matlabcentral/fileexchange/25532-fuzzy-cmeans-segmentation CT image segmentation challenge on the 2019 International Symposium on Image Computing and Digital Medicine. 3 There are 24 training images and 36 testing images, with varied image sizes and a fixed resolution of 1 × 1 × 5 mm 3 . The second dataset come from the MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge, 4 which consists of 131 training images and 70 testing images, with varied image sizes and resolution.…”

Section: Datasets and Evaluation Criteria A Datasetsmentioning

confidence: 99%

“…a need for accurately targeting the tumor area [3]. Computed tomography (CT) is one of the most widely used imaging modalities for liver tumor evaluation and staging [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images

Zhang

Jiang

et al. 2020

IEEE Access

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Liver and liver tumor segmentation provides vital biomarkers for surgical planning and hepatic diagnosis. In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. At the preprocessing step, the CT image intensity values were truncated to lie in a fixed range to enhance the image contrast surrounding liver and liver tumor. To remove non-liver tissues for subsequent tumor segmentation, liver was firstly segmented using two convolutional neural networks in a coarse-to-fine manner. A 2D slicebased U-net was used to roughly localize the liver and a 3D patch-based fully convolutional network was used to refine the liver segmentation as well as to roughly localize the liver tumor. A novel level-set method was then presented to further refine the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor was estimated using unsupervised fuzzy c-means clustering, which was then utilized to enhance the edge-detector used in level-set. Effectiveness of the proposed pipeline was validated on two publiclyavailable datasets. Experimental results identified the superior segmentation performance of the proposed pipeline over state-of-the-art methods.

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“…In unsupervised learning methods, Shan et al [22] proposed a registration method for 2dimensional (2D) CT and Magnetic Resonance Imaging (MRI) medical images. Wei et al [23] used mutualinformation (MI)-based Cycle-GAN to generate synthesized CT (sCT) from pre-procedural MR (pMR) images to convert the multi-modality registration into a mono-modality problem. Balakrishnan et al [24] used a model like U-net [25] named VoxelMorph to directly predicts the DVF and has better performance than Symmetric normalization (SyN) [26].…”

Section: Introductionmentioning

confidence: 99%