Automatic Segmentation and 3D Reconstruction of Spine Based on FCN and Marching Cubes in CT Volumes

Fang, Lingyu; Liu, Jiwei; Liu, Jianfei; Mao, Rui

doi:10.1109/icmic.2018.8529993

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…Deep learning methods have strong feature learning capabilities and can automatically learn effective feature representations from data, thereby improving the accuracy of 3D reconstruction of skeletal tissues. Fang et al [20] proposed a method for spinal segmentation by improving the FCN neural network, which achieved 3D reconstruction of the vertebral body. This method achieved the automation of the 3D reconstruction process of the vertebral column through deep learning, but the complex network structure required signi cant computing resources.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising

Wang

Chen

et al. 2023

Preprint

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The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a rib 3D reconstruction method based on point cloud adaptive smoothing and denoising. We convert the voxel data from the CT images to multi-attribute point cloud data. Then, we apply point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques are employed to achieve high-accuracy and comprehensive rib 3D reconstruction results. Experimental calculations demonstrate that compared to voxel-based rib 3D reconstruction methods, the rib 3D models generated by the proposed method achieve a 40% improvement in reconstruction accuracy and are twice as efficient as the former.

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Section: Introductionmentioning

confidence: 99%

Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising

Wang

Chen

et al. 2023

Preprint

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“…The up-to-date deep learning techniques have been applied to many research fields as a means of generating a model by combining neural network architectures with data. Several volume rendering techniques employ the deep learning techniques, including volume segmentation [4], [5], viewpoint estimation [6], transfer function [7], lighting [8], and quality improvement [9]. These studies utilize Convolution Neural Network (CNN) and Generative Adversarial Net (GAN).…”

Section: Introductionmentioning

confidence: 99%

Image-Based TF Colorization With CNN for Direct Volume Rendering

2021

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In the direct volume rendering (DVR), it often takes a long time for a novice to manipulate the transfer function (TF) and analyze the volume data. To lessen the difficulty in volume rendering, several researchers have developed deep learning techniques. However, the existing techniques are not easy to apply directly to existing DVR pipelines. In this study, we propose an image-based TF colorization with CNN to automatically generate a direct volume rendering image (DVRI) similar to a target image. Our system includes CNN model training, TF labeling, image-based TF generation, and volume rendering by matching the target image. We introduce a technique for training CNN and labeling the TF with images similar to the input volume dataset. Moreover, we extract the primary colors from the target image according to the labels classified with the CNN model. We render the volume data with the TF to produce the DVRI reproducing the prominent colors in the target image.

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Automatic Cobb angle measurement method based on vertebra segmentation by deep learning

Zhao

Zhang

et al. 2022

Med Biol Eng Comput

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Automatic Segmentation and 3D Reconstruction of Spine Based on FCN and Marching Cubes in CT Volumes

Cited by 8 publications

References 2 publications

Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising

Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising

Image-Based TF Colorization With CNN for Direct Volume Rendering

Automatic Cobb angle measurement method based on vertebra segmentation by deep learning

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