A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations

Ernst, Philipp; Hille, Georg; Hansen, Christian; Tönnies, Klaus D.; Rak, Marko

doi:10.1007/978-3-030-32251-9_1

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Overall, the contributions of this work are: (1) We propose TarGAN to generate multi-modality medical images with high-quality local translation on target areas by integrating global and local mappings with a crossing loss. (2) We show qualitative and quantitative performance evaluations on multi-modality medical image translation tasks with CHAOS2019 dataset [12], demonstrating our method's superiority over the state-of-the-art methods. (3) We further use the synthetic images generated from TarGAN to improve the performance of a segmentation task, which indicates that the synthetic images generated by TarGAN achieve the improvement by enriching the information of source images.…”

Section: Introductionmentioning

confidence: 86%

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TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation

Chen

Wei

2021

Preprint

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Paired multi-modality medical images, can provide complementary information to help physicians make more reasonable decisions than single modality medical images. But they are difficult to generate due to multiple factors in practice (e.g., time, cost, radiation dose).To address these problems, multi-modality medical image translation has aroused increasing research interest recently. However, the existing works mainly focus on translation effect of a whole image instead of a critical target area or Region of Interest (ROI), e.g., organ and so on. This leads to poor-quality translation of the localized target area which becomes blurry, deformed or even with extra unreasonable textures. In this paper, we propose a novel target-aware generative adversarial network called TarGAN, which is a generic multi-modality medical image translation model capable of (1) learning multi-modality medical image translation without relying on paired data, (2) enhancing quality of target area generation with the help of target area labels. The generator of TarGAN jointly learns mapping at two levels simultaneously -whole image translation mapping and target area translation mapping. These two mappings are interrelated through a proposed crossing loss. The experiments on both quantitative measures and qualitative evaluations demonstrate that TarGAN outperforms the state-of-the-art methods in all cases. Subsequent segmentation task is conducted to demonstrate effectiveness of synthetic images generated by TarGAN in a real-world application. Our code is available at https://github.com/2165998/TarGAN.

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Section: Introductionmentioning

confidence: 86%

“…Medical imaging, a powerful diagnostic and research tool creating visual representations of anatomy, has been widely available for disease diagnosis and surgery planning [2]. In current clinical practice, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are most commonly used.…”

Section: Introductionmentioning

confidence: 99%

TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation

Chen

Wei

2021

Preprint

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“…They achieved promising results on monomodal fetal MR brain images, which can align motion corrupted MR slices and reduce artifacts in 3D reconstruction. In recent study, Ernst et al 17 proposed a segmentation network to predict the target plane for solving the registration problem. Inspired by his work, our previous work 18,19 proposed U-Net based network yielding a promising registration result.…”

Section: Introductionmentioning

confidence: 99%

Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration

Wei

Rak

et al. 2021

Preprint

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Background and Objective: Ultrasound (US) devices are often used in percutanous interventions. Due to their low image quality, the US image slices are aligned with pre-operative Computed Tomography/Magnetic Resonance Imaging (CT/MRI) images to enable better visibilities of anatomies during the intervention. This work aims at improving the deep learning one shot registration by using less loops through deep learning networks.Methods: We propose two cascade networks which aim at improving registration accuracy by less loops. The InitNet-Regression-LoopNet (IRL) network applies the plane regression method to detect the orientation of the predicted plane derived from the previous loop, then corrects input CT/MRI volume orientation and improves the prediction iteratively. The InitNet-LoopNet-MultiChannel (ILM) comprises two cascade networks, where an InitNet is trained with low resolution images toperform coarse registration. Then, a LoopNet wraps the high resolution images and result of the previous loop into a three channel input and trained to improve prediction accuracy in every loop. Results: We benchmark the two cascade networks on 1035 clinical images from 52 patients , yielding an improved registration accuracy with LoopNet. The IRL achieved an average angle error of 13.3° and an average distance error of 4.5 millimieter. It out-performs the ILM network with angle error 17.4° and distance error 4.9 millimeter and the InitNet with angle error 18.6° and distance error 4.9 millimeter. Our results show the efficiency of the proposed registration networks, which have the potential to improve the robustness and accuracy of intraoperative patient registration.

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Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network

Shen

Wang

et al. 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations

Cited by 9 publications

References 15 publications

TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation

TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation

Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration

Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network

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