The Design of SimpleITK

Lowekamp, Bradley; Chen, David T.; Ibáñez, Luis; Blezek, Daniel J.

doi:10.3389/fninf.2013.00045

Cited by 529 publications

(370 citation statements)

References 13 publications

(12 reference statements)

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“…Then the histogram matching algorithm was applied to transform each scan to a specified histogram using SimpleITK [24]. The images of “brats_2013_pat0001_1” were selected as reference samples.…”

Section: Resultsmentioning

confidence: 99%

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

Chen

Liu

Peng

et al. 2018

International Journal of Biomedical Imaging

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Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

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Section: Resultsmentioning

confidence: 99%

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

Chen

Liu

Peng

et al. 2018

International Journal of Biomedical Imaging

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“…Our implementation of pore network extraction relies on open sources python libraries such as Scipy and ITK [53,54]. The open source functions used are listed in Table 1.…”

Section: Pore Network Extraction Proceduresmentioning

confidence: 99%

Validation of pore network simulations of ex-situ water distributions in a gas diffusion layer of proton exchange membrane fuel cells with X-ray tomographic images

Agaesse

Lamibrac

Büchi

et al. 2016

Journal of Power Sources

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17329 Pore network extraction parameters were benchmarked using full morphology. Keywords:Polymer electrolyte membrane fuel cell Gas diffusion layer X-ray tomographic microscopy Pore network Full morphology Liquid water a b s t r a c t Understanding and modeling two-phase flows in the gas diffusion layer (GDL) of proton exchange membrane fuel cells are important in order to improve fuel cells performance. They are scientifically challenging because of the peculiarities of GDLs microstructures. In the present work, simulations on a pore network model are compared to X-ray tomographic images of water distributions during an ex-situ water invasion experiment. A method based on watershed segmentation was developed to extract a pore network from the 3D segmented image of the dry GDL. Pore network modeling and a full morphology model were then used to perform two-phase simulations and compared to the experimental data. The results show good agreement between experimental and simulated microscopic water distributions. Pore network extraction parameters were also benchmarked using the experimental data and results from full morphology simulations.

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“…Linear interpolation was employed to interpolate the intensities of the source sequence at the intermediate steps of the optimisation. The algorithms were implemented in Python using the SimpleITK open source software library [22]. Details of each experiment are reported in the following sections.…”

Section: Methodsmentioning

confidence: 99%

Registration of Multiview Echocardiography Sequences Using a Subspace Error Metric

Peressutti

Gómez

Penney

et al. 2017

IEEE Trans. Biomed. Eng.

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The Design of SimpleITK

Cited by 529 publications

References 13 publications

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

Validation of pore network simulations of ex-situ water distributions in a gas diffusion layer of proton exchange membrane fuel cells with X-ray tomographic images

Registration of Multiview Echocardiography Sequences Using a Subspace Error Metric

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