Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

Jaffar, M. Arfan; Zia, Sultan; Latif, Ghazanfar; Mirza, Anwar M.; Mehmood, Irfan; Ejaz, Naveed; Baik, Sung Wook

doi:10.1080/18756891.2012.696913

Cited by 22 publications

(4 citation statements)

References 36 publications

(35 reference statements)

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“…Averaging over these curves for each direction gives the proposed smooth EC statistics. It is well known that reconstructing 3D brain tissue (and corresponding tumors) from 2D slices is a nontrivial task [51][52][53]. Moreover, in the context of our case study, it is not guaranteed that the space in-between individual slices will be the same for each patient.…”

Section: Genomic and Radiomic Datamentioning

confidence: 95%

Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis

Crawford

Monod

Chen

et al. 2019

Journal of the American Statistical Association

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Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under active study in the field of cancer biology. Its rapid progression and the relative time cost of obtaining molecular data make other readily-available forms of data, such as images, an important resource for actionable measures in patients. Our goal is to utilize information given by medical images taken from GBM patients in statistical settings. To do this, we design a novel statistic-the smooth Euler characteristic transform (SECT)-that quantifies magnetic resonance images (MRIs) of tumors. Due to its well-defined inner product structure, the SECT can be used in a wider range of functional and nonparametric modeling approaches than other previously proposed topological summary statistics. When applied to a cohort of GBM patients, we find that the SECT is a better predictor of clinical outcomes than both existing tumor shape quantifications and common molecular assays. Specifically, we demonstrate that SECT features alone explain more of the variance in GBM patient survival than gene expression, volumetric features, and morphometric features. The main takeaways from our findings are thus twofold. First, they suggest that images contain valuable information that can play an important role in clinical prognosis and other medical decisions. Second, they show that the SECT is a viable tool for the broader study of medical imaging informatics.

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Section: Genomic and Radiomic Datamentioning

confidence: 95%

Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis

Crawford

Monod

Chen

et al. 2019

Journal of the American Statistical Association

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“…In their approach, brain tumors were segmented using morphological operation, and then a cubic interpolation technique was employed for making the 3D shapes. The model used by [ 35 ] considered a multi-step process for the segmentation and visualization of brain tumors, performed on several datasets. In 2014, P. Kamencay et al [ 37 ] utilized the mean sift method for segmenting the images.…”

Section: Related Workmentioning

confidence: 99%

A GAN-based method for 3D lung tumor reconstruction boosted by a knowledge transfer approach

Rezaei

Ahmadi

2023

Multimed Tools Appl

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Three-dimensional (3D) image reconstruction of tumors has been one of the most effective techniques for accurately visualizing tumor structures and treatment with high resolution, which requires a set of two-dimensional medical images such as CT slices. In this paper we propose a novel method based on generative adversarial networks (GANs) for 3D lung tumor reconstruction by CT images. The proposed method consists of three stages: lung segmentation, tumor segmentation and 3D lung tumor reconstruction. Lung and tumor segmentation are performed using snake optimization and Gustafson-Kessel (GK) clustering. In the 3D reconstruction part first, features are extracted using the pre-trained VGG model from the tumors that detected in 2D CT slices. Then, a sequence of extracted features is fed into an LSTM to output compressed features. Finally, the compressed feature is used as input for GAN, where the generator is responsible for high-level reconstructing the 3D image of the lung tumor. The main novelty of this paper is the use of GAN to reconstruct a 3D lung tumor model for the first time, to the best of our knowledge. Also, we used knowledge transfer to extract features from 2D images to speed up the training process. The results obtained from the proposed model on the LUNA dataset showed better results than state of the art. According to HD and ED metrics, the proposed method has the lowest values of 3.02 and 1.06, respectively, as compared to those of other methods. The experimental results show that the proposed method performs better than previous similar methods and it is useful to help practitioners in the treatment process.

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“…Amruta et al [34] proposed a 3D method for brain tumor recovery in which brain tumors were segmented by morphological manipulations and 3D shapes were generated using 3D interpolation. Jaffar et al [35] considered a multi-step process for segmenting and visualizing brain tumors evaluated on different datasets. Kamencay et al [36] used the medium screening method to segment the images.…”

Section: D Tumor Reconstructionmentioning

confidence: 99%

Combining GAN and LSTM Models for 3D Reconstruction of Lung Tumors from CT Scans

Gu¹,

Gao²

2023

IJACSA

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Generating a three-dimensional (3D) reconstruction of tumors is an efficient technique for obtaining accurate and highly detailed visualization of the structures of tumors. To create a 3D tumor model, a collection of 2D imaging data is required, including images from CT imaging. Generative adversarial networks (GANs) offer a method to learn helpful representations without annotating the training dataset considerably. The article proposes a technique for creating a 3D model of lung tumors from CT scans using a combination of GAN and LSTM models, with support from ResNet as a feature extractor for the 2D images. The model presented in this article involves three steps, starting with the segmentation of the lung, then the segmentation of the tumor, and concluding with the creation of a 3D reconstruction of the lung tumor. The segmentation of the lung and tumor is conducted utilizing snake optimization and Gustafson-Kessel (GK) method. To prepare the 3D reconstruction component for training, the ResNet model that has been pre-trained is utilized to capture characteristics from 2D lung tumor images. Subsequently, the series of characteristics that have been extracted are fed into a LSTM network to generate compressed features as the final output. Ultimately, the condensed feature is utilized as input for the GAN framework, in which the generator is accountable for generating a sophisticated 3D lung tumor image. Simultaneously, the discriminator evaluates whether the 3D lung tumor image produced by the generator is authentic or synthetic. This model is the initial attempt that utilizes a GAN model as a means for reconstructing 3D lung tumors. The suggested model is evaluated against traditional approaches using the LUNA dataset and standard evaluation metrics. The empirical findings suggest that the suggested approach shows a sufficient level of performance in comparison to other methods that are vying for the same objective.

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Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

Cited by 22 publications

References 36 publications

Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis

Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis

A GAN-based method for 3D lung tumor reconstruction boosted by a knowledge transfer approach

Combining GAN and LSTM Models for 3D Reconstruction of Lung Tumors from CT Scans

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