Skull shape reconstruction using cascaded convolutional networks

Kodym, Oldřich; Španěl, Michal; Herout, Adam

doi:10.1016/j.compbiomed.2020.103886

Cited by 23 publications

(15 citation statements)

References 17 publications

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“…Kodym, O. et al [9,8] used cascaded convolutional neural networks (CNN) for skull shape completion, where the first network (3D U-net) takes as input downsampled skull volumes (64 3 ) and produces a coarse output. The second network takes as input the preceding output (upsampled to 128 3 ) as well as an equally sized patch cropped from the original high-resolution skull and produces the final high-resolution output.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

Li¹,

Pepe²,

Gsaxner³

et al. 2021

Preprint

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Medical images, especially volumetric images, are of high resolution and often exceed the capacity of standard desktop GPUs. As a result, most deep learning-based medical image analysis tasks require the input images to be downsampled, often substantially, before these can be fed to a neural network. However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved. In this paper, we propose that high-resolution images can be reconstructed in a coarse-to-fine fashion, where a deep learning algorithm is only responsible for generating a coarse representation of the image, which consumes moderate GPU memory. For producing the high-resolution outcome, we propose two novel methods: learned voxel rearrangement of the coarse output and hierarchical image synthesis. Compared to the coarse output, the high-resolution counterpart allows for smooth surface triangulation, which can be 3D-printed in the highest possible quality. Experiments of this paper are carried out on the dataset of AutoImplant 2021 (https://autoimplant2021.grand-challenge.org/), a MICCAI challenge on cranial implant design. The dataset contains high-resolution skulls that can be viewed as 2D manifolds embedded in a 3D space. Codes associated with this study can be accessed at https://github. com/Jianningli/voxel_rearrangement.

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Section: Related Workmentioning

confidence: 99%

“…For example, in a typical 512 × 512 × 256 volume containing the skull, there may only be around three million of such voxels, which is, however, still impractical for a linear search, as over three million × three million comparisons are needed to update all voxels 9. Not to be confused with the 3 3 and 5 3 voxel neighbors in the image pyramid.…”

mentioning

confidence: 99%

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

Li¹,

Pepe²,

Gsaxner³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The bony midface has a complex structure and is very important for facial appearance and function [ 1 , 2 ]. Various reasons may cause midfacial defects [ 3 , 4 ], and how to restore the normal shape and function of the midface has become an urgent clinical problem [ 5 , 6 ]. Imaging data, such as CT, CBCT, MRI, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Ideal virtual bone reconstruction should base on the remaining bone information to obtain more accurate additional information to repair the defect accurately. The deep learning technology makes the above process become reality, which could learn from a large number of complex samples to find specific rules by imitating the human brain [ 6 , 20 , 21 ]. As a new deep learning algorithm, GAN is widely used in the field of medical data processing, showing excellent image generation ability [ 22 – 24 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual reconstruction of midfacial bone defect based on generative adversarial network

Xiong

Zeng

et al. 2022

Head Face Med

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Background The study aims to evaluate the accuracy of the generative adversarial networks (GAN) for reconstructing bony midfacial defects. Methods According to anatomy, the bony midface was divided into five subunit structural regions and artificial defects are manually created on the corresponding CT images. GAN is trained to reconstruct artificial defects to their previous normal shape and tested. The clinical defects are reconstructed by the trained GAN, where the midspan defects were used for qualitative evaluation and the unilateral defects were used for quantitative evaluation. The cosine similarity and the mean error are used to evaluate the accuracy of reconstruction. The Mann–Whitney U test is used to detect whether reconstruction errors were consistent in artificial and unilateral clinical defects. Results This study included 518 normal CT data, with 415 in training set and 103 in testing set, and 17 real patient data, with 2 midspan defects and 15 unilateral defects. Reconstruction of midspan clinical defects assessed by experts is acceptable. The cosine similarity in the reconstruction of artificial defects and unilateral clinical defects is 0.97 ± 0.01 and 0.96 ± 0.01, P = 0.695. The mean error in the reconstruction of artificial defects and unilateral clinical defects is 0.59 ± 0.31 mm and 0.48 ± 0.08 mm, P = 0.09. Conclusion GAN-based virtual reconstruction technology has reached a high accuracy in testing set, and statistical tests suggest that it can achieve similar results in real patient data. This study has preliminarily solved the problem of bony midfacial defect without reference.

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“…To protect intracranial structures from mechanical impact and achieve aesthetic effects, surgery is usually required to reconstruct the original maxillofacial shape 1,2 . How to repair and reconstruct maxillofacial tissues and organs has become an urgent problem to be solved in clinical 3–5 . Preoperative virtual repair and reconstruction is the most critical and challenging step, and the reconstructed area must match the craniomaxillofacial defects as accurately as possible.…”

Section: Introductionmentioning

confidence: 99%

An intelligent system for craniomaxillofacial defecting reconstruction

Xiong

Guo

et al. 2022

Int J of Intelligent Sys

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Craniomaxillofacial defects caused by congenital or acquired reasons seriously affect patients' physical and mental health. How to accurately and objectively repair the morphology of craniomaxillofacial tissues and organs through surgery is a difficult problem, and the preoperative virtual design is crucial. Traditional preoperative virtual design methods include mirror technology, statistical shape model, and deformable template. However, these methods are complex, time-consuming, and only applicable to some types of defects. Therefore, a general, intelligent, and personalized craniomaxillofacial defect virtual reconstruction system is desired. To solve this problem, a novel deep learning method, RecGAN, is proposed in this paper. RecGAN can learn the bone morphology of normal people, repair the defect intelligently based on the patient's remaining bone, and fully adapt to the special conditions of different patients. Currently, there are no open-source maxillofacial data sets available. Thus, a new maxillofacial computed tomography image data set with 500 simulated cases and 100 clinical cases is constructed to train and validate the method. The experimental results show that RecGAN can effectively restore the normal bone and tissue morphology of the patient's craniomaxillofacial defect area, solve the problem that there is no objective repair method for craniomaxillofacial defect, and achieve the

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Skull shape reconstruction using cascaded convolutional networks

Cited by 23 publications

References 17 publications

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

Virtual reconstruction of midfacial bone defect based on generative adversarial network

An intelligent system for craniomaxillofacial defecting reconstruction

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